M Guironnet
email: mickael.guironnet@yahoo.fr

D Pellerin
email: denis.pellerin@lis.inpg.fr

M Rombaut

A fusion architecture based on TBM for camera motion classification

Keywords: camera motion classification, Transferable Belief Model, motion estimation, motion description, video indexing

We propose in this paper an original method of camera motion classification based on Transferable Belief Model (TBM). It consists in locating in a video the motions of translation and zoom, and the absence of camera motion (i.e static camera). The classification process is based on a rule-based system that is divided into three stages. From a parametric motion model, the first stage consists in combining data to obtain frame-level belief masses on camera motions. To ensure the temporal coherence of motions, a filtering of belief masses according to TBM is achieved. The second stage carries out a separation between static and dynamic frames. In the third stage, a temporal integration allows the motion to be studied on a set of frames and to preserve only those with significant magnitude and duration. Then, a more detailed description of each motion is given. Experimental results obtained show the effectiveness of the method.

INTRODUCTION

Recently, the volume of videos has increased spectacularly with the growth of storage devices and progress of diffusion processes. Consequently this proliferation of video sequences has led to the emergence of new applications such as video summary, classification or browsing in a video base. It has hence become necessary to index video content efficiently to facilitate access to information. Generally, video description relies on the extraction of low-level features such as color, texture or motion to which it is difficult to give a meaning. This description is thus a difficult task which has been studied in many research works.

Among the different features, camera motion is an important index to take into account for video content analysis. From camera motion, much semantic information can be deduced such as the activity in a scene. For example, an action movie contains many scenes with strong camera motions to give rhythm. Furthermore, the way of filming a scene can also direct the gaze. A zoom in will focus the attention on a precise zone of the scene. The knowledge of camera motion can also be exploited to separate moving objects from the background and can be used in the algorithms of segmentation. In this way, this index is an interesting tool to extract the semantic context of the scene.

Using camera motion classification, sport scenes could be labelled such as various cricket shots [START_REF] Lazarescu | On the automatic indexing of cricket using camera motion parameters[END_REF], types of American football moves [START_REF] Lazarescu | Using camera motion to identify different types of american football plays[END_REF] and basketball video sequences [START_REF] Tan | Rapid estimation of camera motion from compressed video with application to video annotation[END_REF]. In the same way, by analyzing the statistics of camera motions, Takagi et al. [START_REF] Takagi | Sports video categorizing method using camera motion parameters[END_REF] show that camera motion is a sufficient signature to differentiate sports activities and classify them. Camera motion can also be used in many applications such as shot segmentation [START_REF] Qi | Sports video categorizing method using camera motion parameters[END_REF][START_REF] Bouthemy | A unified approach to shot change detection[END_REF], video summary [START_REF] Porter | A shortest path representation for video summarisation[END_REF][START_REF] Fauvet | A geometrical key-frame selection method exploiting dominant motion estimation in video[END_REF] as well as in models of visual attention [START_REF] Chend | A visual attention based region-ofinterest determination framework for video sequences[END_REF]. Moreover, requests for video material from archives have become so great that in 2005 a new task was inserted into the TREC Video [START_REF]The nist trec video retrieval evaluation[END_REF] experiments concerning camera motion classification, considering the main objective of TREC Video is to promote progress in content-based retrieval from digital video.

Generally, the dominant motion is assumed to come from camera motion. A parametric model is often used to represent this, and the parameters are estimated either in compressed domain [START_REF] Kim | Threshold-based camera motion characterization of mpeg video[END_REF][START_REF] Tan | Rapid estimation of camera motion from compressed video with application to video annotation[END_REF][START_REF] Gillespie | Robust estimation of camera motion in mpeg domain[END_REF] or in uncompressed domain [START_REF] Porter | A shortest path representation for video summarisation[END_REF][START_REF] Bouthemy | A unified approach to shot change detection[END_REF][START_REF] Fauvet | A geometrical key-frame selection method exploiting dominant motion estimation in video[END_REF]. Other methods obtain camera motions by directly analyzing the MPEG motion vectors [START_REF] Chen | Camera motion classification using a genetic functional-link neural network[END_REF][START_REF] Zhu | Insightvideo: Toward hierarchical content organization for efficient browsing, summarization and retrieval[END_REF][START_REF] Lee | Real-time camera motion classification for content-based indexing and retrieval using templates[END_REF][START_REF] Duan | Mean shift based nonparametric motion characterization[END_REF]. However, most approaches associate a camera motion type from parameters extracted locally (either between two successive frames or from predicted pictures in MPEG) by using a learning algorithm [START_REF] Chen | Camera motion classification using a genetic functional-link neural network[END_REF][START_REF] Duan | Mean shift based nonparametric motion characterization[END_REF] , a strategy of thresholding [START_REF] Kim | Threshold-based camera motion characterization of mpeg video[END_REF][START_REF] Zhu | Insightvideo: Toward hierarchical content organization for efficient browsing, summarization and retrieval[END_REF] or a template-matching algorithm [START_REF] Lee | Real-time camera motion classification for content-based indexing and retrieval using templates[END_REF]. A stage of filtering is sometimes added to obtain consistent motions [START_REF] Zhu | Insightvideo: Toward hierarchical content organization for efficient browsing, summarization and retrieval[END_REF]. However, few methods quantify identified motions. For example, a zoom is detected but the enlargement is not defined, which can be a disadvantage for some applications.

As an alternative to the various approaches presented above, we propose an original method of camera motion classification based on Transferable Belief Model (TBM). This theory is adapted to process imprecise data, to combine various sources of information and to manage the conflict between the sources. The objective of our classification is to label a video in a robust way following the three main camera motion classes which are: translation (pan and/or tilt), zoom and static camera. The translation corresponds either to a rotational motion of the camera about the vertical and/or horizontal axis, or to the tracking of the camera along the vertical and/or horizontal axis. The zoom leads to the enlargement or the reduction of part of the image. Lastly, static camera is a scene obtained with fixed camera where the objects can move. These three camera motions are similar to those of TREC Video 2005 with the determination of the horizontal translation (pan), the vertical translation (tilt) and the zoom. From a parametric motion model, the proposed approach estimates frame-level camera motion, then analyzes segment-level camera motion (on a set of frames). Although the motion estimation used in this work is carried out in uncompressed domain, our method can be adapted to the compressed domain as in [START_REF] Gillespie | Robust estimation of camera motion in mpeg domain[END_REF][START_REF] Kim | Threshold-based camera motion characterization of mpeg video[END_REF][START_REF] Tan | Rapid estimation of camera motion from compressed video with application to video annotation[END_REF]. Indeed, the model parameters which are handled can be indifferently estimated in the compressed or uncompressed domain. The main contribution of this paper resides in the motion recognition that is based on a certain number of rules: combination designed to avoid identifying low magnitude camera motions, a filtering according to TBM to ensure the temporal coherence of the motions, and analysis on segment-level to preserve the motions with consequent magnitude and duration.

The rest of the paper is organized as follows. Section 2 presents an overview of the system architecture for camera motion classification and description. Section 3 discusses motion parameter extraction. After a brief description of the TBM in Section 4, the method of camera motion classification is detailed in Section 5. We explain in Section 6 how identified motions are described. Experimental results are given in Section 7. Finally Section 8 draws the conclusions.

System Overview

The system architecture is depicted in figure 1 and consists of three phases: motion parameter extraction, camera motion classification and motion description. The core of the proposed system is the classification phase which is divided into three stages. The first stage is designed to convert the motion model parameters into symbolic values. This representation facilitates the definition of rules to combine data and to provide frame-level mass func-tions on different camera motions. A filtering of mass functions according to TBM is carried out and contributes to ensuring the temporal coherence of the belief masses. The second stage carries out a separation between static and dynamic (zoom, translation) frames. Finally, in the third stage, the temporal integration of motions is achieved and allows the motions to be studied on segment level (by gathering frames having a certain belief in a type of motion). The advantage of this analysis is to preserve only the motions with significant magnitude and duration. The description phase is then carried out by extracting different features on each video segment containing an identified camera motion type. For example, a zoom motion is characterized by a enlargement coefficient.

Motion parameter extraction

The dominant motion, supposed to come from camera motion, is estimated between two successive frames by a parametric model. The affine model is chosen and can describe 5 traditional types of camera motion: zoom, rotation, horizontal translation, vertical translation, static camera. The velocity vector field is expressed for the pixel position p i = (x i , y i ) of the frame I(p i , t) according to the following equation:

V x (p i ) = c 1 + a 1 • x i + a 2 • y i V y (p i ) = c 2 + a 3 • x i + a 4 • y i
where θ t = (c 1 , c 2 , a 1 , a 2 , a 3 , a 4 ) are the parameters to be estimated. The determination of the model coefficients is carried out by the Motion2D software [START_REF] Odobez | Robust multiresolution estimation of parametric motion models[END_REF]. It yields a robust and incremental estimation of the dominant motion exploiting the spatio-temporal derivatives of the frame intensity.

Before to use these coefficients, an average filter of size L 1 on the parameters θ t is achieved in order to reduce noise and estimation errors. An example of the parameter estimation is shown in figure 2 where the sequence contains a zoom in. It can be seen that the parameters are disturbed and sometimes erroneous (strong impulses between image 400 and 450). The temporal window size is chosen by expertise to 13 frames corresponding to half of a second which is coherent to the human reflex time. 2. The parameters (in pixels/frame) evolve according to time on a sequence containing a zoom in. As regards the zoom motion, the only parameter expected to be higher than 0 is (a 1 + a 4 )/2 and the parameters c 1 and c 2 are expected to be null.

Some model parameters are specific to a motion and are used to identify camera motions. From the filtered parameters θ t = (c 1 , c 2 , a 1 , a 2 , a 3 , a 4 ), the displacement of the camera dpl(t) and the divergent div(t) between two successive frames I(p i , t) and I(p i , t + 1) are defined as well as the total displacement dpt(t o , t f ) and the distance traveled dtl(t o , t f ) between two times t o and t f :

-→ dpl(t) = (c 1 (t), c 2 (t)) dpl(t) = -→ dpl(t) div(t) = 1 2 (a 1 (t) + a 4 (t)) dpt(t o , t f ) = t f -1 j=to -→ dpl(j) dtl(t o , t f ) = t f -1 j=to -→ dpl(j)
The total displacement dpt in pixels/frame corresponds to the displacement in the straight line between the original and final position whereas the distance traveled dtl is the original way and corresponds to the integration of all displacements between sampling times.

According to the magnitude of the variables div and dpl, the different camera motions can be extracted. A translation (respectively a zoom) is detected if the displacement (respectively the divergent) is high. When a light zoom and a strong translation occur simultaneously, the zoom is not, or hardly visible and thus should not be highlight. In the same way, only the zoom is preserved in the presence of a strong zoom and a weak translation. In order to satisfy these rules, the variables need to be converted into linguistic values to be combined. Before describing camera motion classification, the following section will point out the bases of the Transferable Belief Model.

Transferable Belief Model

The Transferable Belief Model was formalized by P. Smets [START_REF] Smets | The transferable belief model[END_REF] and comes from the Dempster-Shafer's evidence theory.

Let Ω = {H 1 , • • • , H N } be the frame of discernment containing N mutually exclusive and exhaustive hypotheses related to a given problem. From the frame of discernment, the power set of Ω denoted as 2 Ω is defined and is composed of all subsets of Ω (singleton and composed hypotheses).

2

Ω = {A/A ⊆ Ω} = {∅, {H 1 }, • • • , {H N }, {H 1 , H 2 }, • • • , Ω}
It is assumed that the solution to a given problem is necessarily in the frame of discernment (closed world). On the contrary, the open-world assumption admits the existence of hypotheses not defined in the frame of discernment.

A mass function or a Basic Belief Assignment (BBA) is a function m : 2 Ω → [0, 1] that assigns a value in [0,1] to each subset A of Ω. The value m(A) is the part of belief that is allocated exactly to the proposition A. Under closedworld assumption, a BBA is subject to the following constraints: m(∅) = 0 and A⊆Ω m(A) = 1. The subsets A ⊆ Ω such that m(A) > 0 are called focal elements of m.

Consider two BBA m 1 and m 2 defined on the same frame of discernment and provided by a source 1 and a source 2 respectively. According to applications, two combinations are possible: conjunctive combination

m 1 ∩ m 2 (A i ) and dis- junctive combination m 1 ∪ m 2 (A i ). m 1 ∩ m 2 (A i ) = A j ∩A k =A i m 1 (A j ) • m 2 (A k ) m 1 ∪ m 2 (A i ) = A j ∪A k =A i m 1 (A j ) • m 2 (A k )
The conjunctive combination (respectively disjunctive) is interpreted as a logical "and" (respectively "or"). These combinations can then be used in logical rules.

From a BBA, a transformation was proposed by P. Smets [START_REF] Smets | The transferable belief model[END_REF] to obtain a probability measure called pignistic probability on the frame of discernment Ω:

BetP Ω (A) = B⊆Ω m Ω (B) 1 -m Ω (∅) |A ∩ B| |A| , ∀A ⊆ Ω ( 1
)
where |A| is the cardinal of A ⊆ Ω. This function can be used for decisionmaking.

Let Ω 1 and Ω 2 be two distinct and disjointed frames of discernment, a BBA can be defined on Ω = Ω 1 ×Ω 2 through the conjunctive combination as follows:

m 1 ∩ m 2 (A × B) = m 1 (A) • m 2 (B) ∀A ⊆ Ω 1 , ∀B ⊆ Ω 2
The interest of the cartesian product is to apply TBM even when the frames of discernment are disjointed and thus not compatible.

Camera motion classification

Camera motion classification consists in locating in a video the places where a camera motion takes place. The method, which depends on TBM, has to identify the three camera motions that are translation, zoom and the absence of motion. It also has to recognize strong and short motions as well as weak and long motions, and to avoid false detections due to a poor estimation. The principle of camera motion classification phase is presented in figure 3. It is divided into three stages: combination based on heuristic rules, static/dynamic separation and temporal integration of zoom and translation motions. The sequel of this section describes each stage of the method.

Numerical symbolic conversion div dlp m dlp 2,t m div 1,t
Temporal filter of the mass functions Static rule The first stage (fig. 3) consists in converting the model parameters into symbolic values describing the retrieved motions. From these variables, we establish heuristic rules to combine them in order to give frame-level belief masses on the different camera motions. Then a temporal filtering of belief masses is carried out for ensuring the temporal coherence of the belief masses on a neighborhood.

m 3,t x A B Fusion m 4,t x A B m 5,t x A B m 7,t x A B m 6,t x A B Integration of zoom m 9,t x A B m 8,t x A B

Integration

Numerical-symbolic conversion

The numerical variables dpl and div are transformed into symbolic values: weak (W), average (A), large (L) and very large (VL). A type of fuzzy sets is used to formalize expert knowledge and to provide a symbolic representation of data. Each linguistic term or group is associated to a set defined by a function as indicated in figure 4. With regard to the symbolic description of divergent, it is carried from the absolute value of divergent (4.a). Indeed, the absolute value only gives information about the amplitude of the zoom whereas the direction of the zoom is obtained by the sign. Thus the mass functions for the variables div and dpl are respectively defined on the frame of discernment div = {W, A, L, V L} and dpl = {W, A, L, V L}. The combination of these mass functions will lead to camera motion detection. m 1 Because it is difficult to get annotated data base, the thresholds are fixed by expertise. The TBM framework allows the expert to directly model the doubt by union of hypotheses: a large doubt avoid risk but does not give lot of information.

0 10 12 W A [ L L W [ A A L [ VL V L

Inference rules

The approach to camera motion classification is based on heuristic rules. The Transferable Belief Model (TBM) provides tools adapted to build models that integrate inference mechanisms.

Let A = {T, T } be the frame of discernment of the translation motion and let B = {Z, Z} be the frame of discernment of the zoom motion where T (resp Z) is a hypothesis on the presence of the translation (resp zoom) and T (resp Z) is an hypothesis on the absence of the translation (resp absence of zoom). The motion identification can be carried out by applying the cartesian product of sets A × B. For example, if a frame belongs to class (T , Z) then the frame is regarded as static. On the other hand, if the frame belongs to class (T, Z) then the frame has a camera motion at the same time of translation and zoom.

We define the set of rules R to attribute belief masses to the product set A×B:

• If div is weak and dpl is weak then the camera motion is static (T , Z).

• If div is average and dpl is large then the motion detected is translation (T, Z). • If div is very large and dpl is very large then the motion detected is translation and zoom (T, Z). • and so on.

These rules R defined for camera motion classification are summarized in table 1. For example, if div is large and dpl is average then detected motion is zoom and thus the belief mass on the product set A × B is assigned to (T , Z). We can also notice propositions on several motions such as {(T , Z), (T , Z)}. This means the absence of translation and ignorance of the presence of zoom, which corresponds to a proposition on "static or zoom". In the same way, {(T , Z), (T, Z)} means the absence of zoom and ignorance of the presence of translation, which corresponds to a proposition on "static or translation" whereas A × B is a total ignorance of the camera motion. The combination m div 1 and m dpl 2 with the rules R leads to the definition of a new BBA m A×B 3 = m div 1 R m dpl 2 which directly characterizes the belief on camera motions. It can also be noticed that the rules are designed to avoid as far as possible secondary motions. For example, if the displacement is very large and the divergent is large then the zoom motion is neglected and only the translation motion is considered. Finally, a BBA m A×B 3,t is obtained for each frame t of the video.

div W eak Average Large V ery Large dpl W eak (T , Z) (T , Z), (T ,Z) (T , Z) (T , Z) Average (T , Z), (T, Z) A × B (T , Z) (T , Z) Large (T, Z) (T, Z) (T, Z) (T , Z) V ery Large (T, Z) (T, Z) (T, Z) (T, Z)
Table 1 Attribution rules R according to divergent div and displacement dpl

In order to explain the method, we propose to process the example in which, for a given image, the non null masses are m div 1 and m dpl 2 as follows:

m div 1 ({W }) = 0.7 m dpl 2 ({A, L}) = 0.2 m div 1 ({W, A}) = 0.3 m dpl 2 ({L}) = 0.8
The BBA m A×B 3 has 3 focal elements:

m A×B 3 (T , Z), (T, Z) = m div 1 ({W }) • m dpl 2 ({A, L}) = 0.14 m A×B 3 (T, Z) = m div 1 ({W }) • m dpl 2 ({L}) + m div 1 ({W, A}) • m dpl 2 ({L}) = 0.80 m A×B 3 (A × B) = m div 1 ({W, A}) • m dpl 2 ({A, L}) = 0.06

Temporal filtering of mass functions

Temporal filtering of mass functions was introduced and is based on the hypothesis that camera motion cannot be very different from one frame to the next. If the case appears, then it is considered that all motions are possible, without being able to highlight one rather than another. This filtering according to TBM adds doubt by reallocating the belief on the union of motion propositions if the temporally close beliefs deliver different information. The filter is produced by the disjunctive combination of the sources m A×B 3,t on a temporal window of size L 2 . A new BBA is then obtained:

m A×B 4,t = m A×B 3,t-(L 2 -1)/2 ∪ . . . ∪ m A×B 3,t+(L 2 -1)/2
The interest of this combination is to increase the temporal coherence of motions and thus prevent the presence of different motions on a neighborhood.

The consistency of a motion can be improved by filling the possible holes generated by estimation errors.

Figure 8-a shows an example of sequence having a zoom out where the method is applied with a window of size L 1 = L 2 = 13. When the divergent is average and the displacement is weak, the motion is considered to be "static or zoom" {(T , Z), (T , Z)} on curve m A×B 3,t . When the displacement becomes average with an average divergent, the mass is allocated to total doubt A×B. The temporal filtering (curve m A×B 4,t ) amplifies the zone of A × B by adding doubt. The two following stages of classification allow camera motion to be found.

Globally, the rules and the filtering correspond to a very cautious process leaving a wide place open to doubt rather than imposing a final decision on camera motion.

Static/dynamic separation (stage 2)

The second stage (fig. 3) consists in separating the static frames from the dynamic frames (zoom, translation) by taking into account the temporal neighborhood of beliefs allocated locally by the heuristic rules (here the preceding filtering is not considered). In the absence of camera motion, the estimated model parameters have often a weak magnitude. However this property is not always checked locally because of noise or estimation errors. To take it into account, a frame will be considered as static if the majority of close frames are static. Thus a new BBA is defined and is based on the following rule: if a certain number of frames around the frame studied have a belief on the static hypothesis (respectively dynamic) then a belief mass will be allocated to the static hypothesis (respectively dynamic) for the frame studied.

Let Ω = {S, D} be the frame of discernment where S and D indicate a static and dynamic motion respectively. Ω is a coarsening of A × B and reciprocally A × B is a refinement of Ω. In order to know if the frame t is rather static or rather dynamic, each BBA m A×B 3,t is transformed into a BBA m Ω 3,t as follows:

m Ω 3,t ({S}) = m A×B 3,t ({(T , Z)}) m Ω 3,t ({D}) = K⊆A×B\{(T ,Z)} m A×B 3,t (K) m Ω 3,t (Ω) = 1 -m Ω 3,t ({S}) -m Ω 3,t ({D})
From the previous example,

m A×B 3 (T , Z), (T, Z) = 0.14 m A×B 3 (T ,Z) = 0.80 m A×B 3 (A × B) = 0.06 mass m Ω 3 is obtained: m Ω 3 ({S, D}) = m A×B 3 (T , Z), (T, Z) + m A×B 3 (A × B) = 0.20 m Ω 3 ({D}) = m A×B 3 (T ,Z) = 0.80
For each frame t, a temporal window of size L 3 centered on t is considered. The new BBA for the frame t defined on Ω is deduced from the conjunctive combination of the BBAs of all the frames of the window and defined on the cartesian product Ω = Ω t-(L 3 -1)/2 × . . . × Ω t+(L 3 -1)/2 where each Ω i is associated to frame i of window centered on frame t studied. The coarsening process from Ω to Ω depends for each subset of Ω on the number of frames that are considered to be static (S), dynamic (D) or doubt between these hypotheses. If the subset has at least α% of frames on the static hypothesis, then this combination mass is deferred to the static hypothesis S for the frame t studied. In the same way, if the subset of Ω has at least 100 -α% of frames on the dynamic hypothesis then it is affected to the dynamic hypothesis D for the frame t studied. If it is not the case, then the mass is returned to the set of hypotheses {S, D}.

The number of images attributing a mass to the static hypothesis is determined by n = ceil(L 3 .α) where ceil means round up to nearest integer. The number of images with a mass on the dynamic hypothesis is obtained by

L 3 -n + 1.
The value of α = 50% is a compromise between the ability of detection of real static frames and the risk of false detection.

Let us take the example with L 3 = 3 and α = 50% with Ω

1 = {S 1 , D 1 }, Ω 2 = {S 2 , D 2 } et Ω 3 = {S 3 , D 3 }.
The number of images that must affect a mass to the static hypothesis is n = 2, whereas for the dynamic hypothesis the number of images is of L 3 -n + 1 = 2. The belief mass distribution of image 2 is modified according to beliefs of close images (here image 1 and 3). An example is shown in equation 2 how the proposition of cartesian product Ω is redistributed on Ω:

{S 1 } × {D 2 } × {S 3 } → {S} {S 1 } × {D 2 } × {D 3 } → {D} {S 1 } × {D 2 } × {S 3 , D 3 } → {S, D} (2)
If on three successive images, there is a strong belief mass related to static hypothesis for image 1 and 3, and a strong belief mass allocated to dynamic motion for image 2:

m Ω 1 3 ({S 1 , D 1 }) = 0.3 m Ω 1 3 ({S 1 }) = 0.7 m Ω 2 3 ({S 2 , D 2 }) = 0.4 m Ω 2 3 ({D 2 }) = 0.6 m Ω 3 3 ({S 3 , D 3 }) = 0.1 m Ω 3 3 ({S 3 }) = 0.9
The BBA m Ω 5 then is obtained:

m Ω 5 ({S}) = m Ω 1 3 ({S 1 }) • m Ω 2 3 ({S 2 , D 2 }) • m Ω 3 3 ({S 3 })+ m Ω 1 3 ({S 1 }) • m Ω 2 3 ({D 2 }) • m Ω 3 3 ({S 3 }) = 0.63 m Ω 5 ({S, D}) = 0.37 m Ω 5 ({D}) = 0
This example shows that the distribution of the masses depends on the proportion of beliefs on the static and dynamic hypotheses. As two images out of three have a strong belief on the static hypothesis, the resulting mass supports the belief on the static hypothesis and the mass allocated to D is null.

Based on this rule, a BBA m Ω 5,t on Ω is defined for each frame t. It is extended to A × B using the relations {(T , Z)} = S, {(T , Z), (T, Z), (T, Z)} = D and A × B = Ω, and it is combined with m A×B 4,t using the conjunctive combination. The resulting BBA is m A×B 6,t and if the mass attributed to the empty set is nonnull then it is transferred to the union of propositions. Table 2 recapitulates the combination of the two BBA. using the rule of conjunctive combination and managing the empty set.

m A×B 5,t (T , Z) (T, Z), (T , Z), (T, Z) A × B m A×B 4,t (T , Z) (T , Z) ∅ → A × B (T , Z) (T, Z) ∅ → (T, Z), (T , Z) (T, Z) (T, Z) (T , Z) ∅ → (T , Z), (T , Z) (T , Z) (T , Z) (T, Z) ∅ → (T, Z), (T , Z) (T, Z) (T, Z)
Figure 5 illustrates the second stage of this approach with the following parameters α = 50% and L 3 = L 2 = L 1 = 13. The sequence is filmed with fixed camera. For example, a belief mass is assigned to the proposition "zoom or statics" (between the images 85 and 105 , and images 38 and 42 of m A×B 4,t ) and the dynamic/static separation redistributes the belief mass to the "static" proposition on the curve m A×B 5,t . On the other hand, the dynamic/static separation is not sufficient to allocate the belief to the "static" proposition on all along the segment. Indeed, for example, no mass is associated to the "static" proposition between images 1 and 10 of m A×B 3,t and the resulting mass (curves m A×B 4,t ) is assigned with the "static or dynamic" proposition. It is the integration of this segment which will find the static camera. In figure 8-b, as the motion is either "static or zoom" (T , Z), (T , Z) or total doubt A×B, the separation cannot find static camera since no mass is associated to the proposition "static".

Temporal integration of zoom and translation (stage 3)

The third stage (fig. 3) achieves a more global motion description on segment level (by gathering frames containing the same motion type). This consists in segmenting the sequence by coherent motions (translation or zoom), then estimating the motion magnitude on each segment. By describing motion on each segment, the purpose of this integration is to preserve only motions of consequent magnitude and duration.

Case of zoom

As soon as the pignistic probability BetP A×B ({(T , Z), (T, Z)}) on a frame (eq. 1) becomes higher than a threshold δ then the beginning of zoom is detected and this time t 0 is memorized. When BetP A×B ({(T , Z), (T, Z)}) is lower than δ then the zoom motion stops and this time t f is memorized. The segment between t 0 and t f contains a potential zoom which is analyzed to be ensured of its presence. In order to detect this potential motion, δ is chosen sufficiently low. As the divergent is not very well adapted to represent zoom, the enlargement coefficient is introduced.

We develop the case in one dimension. Let a 1 (t) be the parameter of the affine model at the time t and let v x be the velocity for the position x i provided by v x = a 1 (t) • x i assuming the other coefficients to be null (case for a perfect zoom). The position at the time t + 1 is given by

x i = x i + v x = x i • (1 + a 1 (t)).
From where the ratio between the position at the final time t f and the position at the initial time t 0 is given by:

k x = t f -1 t=t 0 (1 + a 1 (t))
If the motion is a zoom in, the ratio k x corresponds to an enlargement coefficient (k x > 1), ag x . On the other hand, if it is a zoom out then k x is a reduction coefficient (k x < 1) and by convention the inverse of this ratio ag x = 1/k x is called enlargement coefficient. In the case of a frame (2 dimensions), one enlargement coefficient ag x is defined along the x-axis and one ag y following the y-axis. To obtain only one enlargement coefficient ag, the two coefficients ag x and ag y are multiplied. The enlargement coefficient ag represents the ratio between frame size and the part of the frame that increased until frame size. For zoom segment, the sign of divergent can change, which means a change of zoom direction. In order to take this into account, we determine on each zoom segment, the sub-segments having the divergent of the same sign and an enlargement is calculated on each one of them.

Finally, the enlargement coefficient ag that characterizes the power of the zoom is used to cancel or preserve the zoom on the segment or the sub-segment. Thus, a BBA m Ω Z m Ω Z 7 is then extended on A × B using the relations {(T , Z), (T, Z)} = Zoom, {(T , Z), (T, Z)} = Zoom and A × B = Ω Z and the resulting BBA m A×B 7 is associated to each frame of the segment. The passage of description from segment to frame allows the BBA m A×B 6,t defined previously to be combined with this one and the resulting BBA is m A×B 8,t . Table 3 shows the combination of the masses. It is important to note that, in case of conflict, m A×B 7,t being more reliable than m A×B 6,t for the "zoom", the mass associated to the empty set is transfered to the proposition of the zoom coming from m A×B 7,t and to the proposition of the translation coming from m A×B 6,t .

m A×B 6,t (T , Z) (T, Z) (T , Z) (T, Z) m A×B 7 (T , Z), (T, Z) (T , Z) (T, Z) ∅ → (T , Z) ∅ → (T, Z) (T , Z), (T, Z) ∅ → (T , Z) ∅ → (T, Z) (T , Z) (T, Z) A × B (T , Z) (T, Z) (T , Z) (T, Z)
Table 3 Combination of the mass functions m A×B 6,t and m A×B 7

Case of translation

As processing with zoom, a segment of potential translation is obtained using BetP A×B ({(T, Z), (T, Z)}) > δ, then the segment between t o and t f is analyzed by calculating maximum displacement dpt max on this window.

t = arg max t k ∈[to,t f ] (dpt(t o , t k )) and dpt max = dpt(t o , t)
Maximum displacement dpt max is then standardized by the duration (from time t o to t) to have a relative representation of displacement. Thus standardized maximum displacement dpt maxn characterizes the power of translation on the segment and this value is used to define a mass function (fig. 7) on Ω T = {T ranslation, T ranslation}. Like the zoom, m Ω T 9 is extended on A × B, then this one is associated to each frame of segment to be combined with m A×B 8,t and the resulting BBA is m A×B 10,t .

The integration is applied with δ = 0.1 in figure 8-c. In fact, if δ is low, the motion detection is improved. That can lead to false alarms, but in this

dpt maxn 0 1 1 0 1.5 0.5 m Ω T 9
Translation Ω T Translation Fig. 7. Definition of the BBA for the standardized maximum displacement algorithm, the temporal integration insures the reliability. We can see that the integration of the zoom (curve m Ω Z 7,t ) allows it to be preserved and thus leads to the removal of the "static" proposition on the curve m A×B 8,t . Then, the integration of the translation (curve m Ω T 9,t ) allows it to be removed and thus to only the proposition (T , Z) on the curve m A×B 10,t is preserved.

Finally the decision on camera motions is taken by choosing the maximum of the pignistic probability for each frame.

Camera motion description

This phase (fig. 1) consists in describing each identified camera motion. For the three motions (static, translation and zoom), a binary decision is attributed to each frame. Based on the results of the previous paragraphs, each segment where a zoom is identified is described by the enlargement coefficient and the direction. The sign of the divergent is used to know the zoom direction (zoom in or zoom out). The translation segment is represented by distance traveled and standardized total displacement. Moreover, the translation direction is obtained for each frame contained in a translation segment. A fuzzy quantification (fig. 9) from vector phase -→ dpl(t) is used to represent it. For example, a diagonal motion from down-left to up-right is characterized by the four values (Zone 1, Zone 2, Zone 3, Zone 4) = (0.5, 0.5, 0, 0).

Camera motion classification evaluation

Camera motion classification evaluation aims to verify the performance of the method. Two studies are discussed: one on video extracts containing a single camera motion and an other containing composed camera motions. Thereafter, we apply the method with the following thresholds: L 1 = L 2 = L 3 = 13 (window about a half second), α = 50% (stage 2) and δ = 0.1 (stage 3). Fig. 9. Membership functions according to the 4 directions.

Analysis of single motions

To evaluate the method of camera motion classification, video extracts containing a single camera motion type were selected during the video playback. The results are reported for motion classification (presence of static, translation or zoom). Like evaluation measures, we use recall and precision. Recall R evaluates the capacity of the classifier to find the videos in the base containing a retrieved motion and is defined as the number of relevant video extracts retrieved containing the desired motion in a database divided by the total number of video extracts retrieved. Precision P evaluates the capacity of the classifier to find only videos having the desired motion and is defined as the number of relevant video extracts retrieved containing the desired motion divided by the total number of relevant video extracts in a database. However, the classification of a video extract depends on camera motion allocated on each one of these frames. We consider that a video is correctly identified if all frames are correctly classified. Table 4 shows the results of motion classification. If the zoom is considered, recall indicates that one video is not found. It is about a zoom which is in fact detected at 73%. The beginning of this video has a light zoom and is related with a static camera. With regard to the translation motion, it misses one video for the recall, this one is detected at 95% and has a small static segment at the beginning. Hence camera motion classification presents good performances with a precision of 100%, a recall > 92% for the three camera motions, which demonstrates the robustness of the method. Performance of the description of zoom and translation according to direction with recall and precision calculated at 80%

Analysis of composed motions

Camera motion classification is studied here on video extracts where the motions can be superimposed (zoom and translation) or successive in the same extract. Figure 11 shows an example of video extract including several camera motions, initially a segment of zoom out and translation of left to right, then a segment of zoom and finally a static segment. This example also underlines the camera motion description. The indicators of the different camera motions can also be found (4 directions of the translation, the distance traveled, standardized total displacement as well as the direction of the zoom and the enlargement coefficient).

To evaluate the method, we annotated three video extracts according to the three camera motions:

• a sports documentary with 20 shots and 3271 frames • "The Avengers" series with 27 shots and 2412 frames • TV news with 42 shots and 6870 frames By assuming the known shots, the different motions are extracted by the method and compared with the ground truth. The evaluation is carried out by recall and precision on frame level (calculation of the frame number correctly identified for each motion). Nevertheless, the ground truth is sometimes difficult to determine in certain places of the video (ambiguity between motions) or the border between two successive motions is difficult to find. From these considerations, errors can be added to the classification errors coming from the classification method. That allows the results presented in table 6 to be moderated. We can note that the results are good with more than to 70% recall and precision for the three videos. With regard to static, it is dominating in the three videos and is detected with good accuracy. The motion of translation is also easily found in the videos. On the other hand, the motion of zoom is the least present in the videos. Considering this small quantity, the results are relatively good with more than to 70% recall and precision. Figure 12 is an example of the camera motion classification and corresponds to the first shot of the "The Avengers" sequence where a translation motion is followed by a static segment. We can notice that the motions identified by the method are similar to those of the ground truth. As the border between the motions is not exactly at the same place, the recall and the precision are 81% and 100% for statics and 100% and 95% for the translation whereas the motion determination seems to be correct.

Conclusion

A method of camera motion classification based on Transferable Belief Model has been presented. It consists in finding the motions of translation and zoom, and static camera in a video. The approach is characterized by its rule-based recognition system. The combination rules are designed to avoid as far as possible secondary motions (low magnitude motions). A filtering according to TBM is carried out and modifies the belief in a motion following the close frames. A static/dynamic separation is archived and assumes that a frame is static if these close frames are considered to be static. Finally the analysis on segment level aims at only preserving motions of consequent magnitude and duration. Then, the description of motions is carried out by quantifying them (for example, enlargement coefficient for a zoom) to interpret them easily. The motion of translation and zoom are also characterized in a more local way with the direction (zoom in, zoom out, translation from left to right. . . ).

In order to ensure the performances of the method, we have presented results on videos containing one motion type or containing superimposed camera motions or which followed one another. In the two cases, the results obtained in term of recall and precision enable us to conclude that our classifier is effective to determine camera motions. One of the future lines of investigation would be to consider other motion types such as rotation. The advantage of the TBM framework is that it is easy to add new parameters without changing the structure of the system. If it is assumed the rotation is relevant enough, the associated parameters could be included. Lastly, our method requires knowledge of shot change. A detector could also be designed from camera motion.

Stage 3 :Phase 2 : 1 : 3 : 1 :Fig. 1 .

 321311 Fig. 1. System architecture for camera motion classification and description

  Fig.2. The parameters (in pixels/frame) evolve according to time on a sequence containing a zoom in. As regards the zoom motion, the only parameter expected to be higher than 0 is (a 1 + a 4 )/2 and the parameters c 1 and c 2 are expected to be null.

Fig. 3 .

 3 Fig. 3. Principle of camera motion classification phase

Fig. 4 .

 4 Fig. 4. Definition of the BBAs for the displacement dpl (a) and for the divergent div (b) in pixels/frame

Fig. 5 .

 5 Fig. 5. Stage 2: Illustration of filtering and static/dynamic separation on a filmed sequence with fixed camera. The expected motion is static (T , Z).

Fig. 6 .

 6 Fig. 6. Definition of BBA for the enlargement coefficient.

3 Fig. 8 .

 38 Fig.8. Illustration of the classification method (stages 1, 2 and 3) on a sequence having a motion of zoom out. The expected motion is (T , Z). The variables div and dlp are in pixels/frame.

Fig. 10 .

 10 Fig. 10. Examples of video extracts contained in the base. For each example, the left image corresponds to the first image of the extract and the right image is the last image of the extract. Two examples are filmed with fixed camera (in top), two include a translation motion (in the medium) and finally two contain a zoom (in bottom).

Fig. 11 .

 11 Fig. 11. Classification on an video extract including several camera motions.

Fig. 12 .

 12 Fig. 12. Example of camera motion classification on the first shot of the "The Avengers" sequence.

Table 2

 2

	Combination of BBA m A×B 5,t	and m A×B 4,t

Table 4

 4 Performance of the classification of video extracts

Table 5

 5 illustrates the description of zoom and translation according to the direction. Here, a video is correctly identified if at least 80% of frames are well classified. The obtained results shows the performance of the motion direction description.

		Right	Up	Left to	Down	Zoom	Zoom
		to left	to	right	to up	in	out
			down
	Recall at	100	100	100	100	100	83
	80%	(6/6)	(7/7)	(6/6)	(2/2)	(7/7)	(5/6)
	Precision	100	100	100	100	100	100
	at 80%	(6/6)	(7/7)	(6/6)	(2/2)	(7/7)	(5/5)

Table 5

 5

Table 6

 6 Performance of the classification of frames on three video extracts with composed motions

is built on the frame of discernment Ω Z = {Zoom, Zoom} from the enlargement coefficient as shown in figure6.


M Guironnet
email: mickael.guironnet@yahoo.fr

D Pellerin
email: denis.pellerin@inpg.fr

N Guyader

P Ladret

Video summarization based on camera motion and a subjective evaluation method

Keywords: Camera motion, video summary, keyframe selection, succession and magnitude of motions, evaluation method of summaries

In this paper, we propose an original method of video summarization based on camera motion. It consists in selecting frames according to the succession and the magnitude of camera motions. The method is based on rules to avoid temporal redundancy between the selected frames. We also develop a new subjective method to evaluate the proposed summary and to compare different summaries more generally. Subjects were asked to watch a video and to create a summary manually. From the summaries of the different subjects, an "optimal" one is built automatically and is compared to the summaries obtained by different methods. Experimental results show the efficiency of our camera motion-based summary.

Introduction

During this decade, the number of videos has increased with the growth of broadcasting processes and storage devices. To facilitate access to information, various indexing techniques using "low-level" features such as color, texture or motion have been developed to represent video content. It has led to the emergence of new applications such as video summary, classification or browsing in a video database. In this paper, we will introduce two methods required to study video summary: the first one explains how to create a video summary and the second one how to evaluate it and to compare different summaries.

A video summary is a short version of the video and is composed of representative frames, called keyframes. The selection of keyframes has to be done with the aim of both representing the whole video content and suppressing the redundancy between frames. As we said, videos are usually described by "low-level" features to which it is difficult to give a meaning. On the contrary, a semantic meaning can be deduced from camera motions. For example, an action movie contains many scenes with strong camera motions: a zoom-in will focus the spectator's gaze on a particular location in a scene. In this paper, we exploit the information provided by camera motion to describe the video content and to choose the keyframes.

In the literature, some video summary methods were proposed from camera motion. The first family uses camera motion to segment the video but not to select the keyframes. The keyframe selection is based on other features. In [START_REF] Kopf | Automatic generation of video summaries for historical films[END_REF], the camera motion is used to detect moving objects and this information is used to build the summary. In [START_REF] Ma | Video snapshot: A bird view of video sequence[END_REF], camera motion is used to partition the shots in segments and keyframe selection is carried out with other indexes (4 basic measures, i.e. visually pleasurable, representative, informative and distinctive). A shot is, by definition, a portion of video filmed continuously without special effects or cuts, and a segment is a set of successive frames having the same type of motion. In [START_REF] Zhu | Insightvideo: toward hierarchical video content organization for efficient browsing, summarization and retrieval[END_REF], shots are segmented according to camera motions. Then, MPEG motion vectors, that contain the camera and object motions, are used to define the motion intensity per frame and select the keyframes. Nevertheless, these approaches do not select keyframes directly according to camera motion. In fact, the camera motion is used more to segment the video than to create the summary itself.

The second family is based mainly on the presence or the absence of motion. Cherfaoui et al. [START_REF] Cherfaoui | Two-stage strategy for indexing and presenting video[END_REF] detect the shots, then determine the presence or the absence of camera motion. The shots with a camera motion are represented by three keyframes, whereas the shots with fixed camera have only one. Peker et al. [START_REF] Peker | An extended framework for adaptive playbackbased video summarization[END_REF] work out a summary method by selecting the segments with large motions in order to capture the dynamic aspects of video. In this case they used camera motion and also object motion. In [START_REF] Kaup | Video analysis for universal multimedia messaging[END_REF], the segments with a camera motion provide keyframes which are added to the summary. Nevertheless, these approaches are based on simple considerations which exploit little information contributed by camera motion.

The third family uses camera motion to define a similarity measure between frames; this similarity is then used to select the keyframes. In [START_REF] Porter | A shortest path representation for video summarisation[END_REF], a similarity measure between two frames is defined by calculating the overlap between them. The greater the overlap is, the closer the content is and the fewer keyframes are selected. In the same way, Fauvet et al. [START_REF] Fauvet | A geometrical key-frame selection method exploiting dominant motion estimation in video[END_REF] determine from the estimation of the dominant motion, the areas between two successive frames which are lost or appear. Then, a cumulative function of surfaces which appear between the first frame of the shot and the current frame is used to determine the keyframes. Nevertheless, these approaches are based on a low level description which measures the overlap between frames. They are based on geometrical and local properties (number of pixels which appear or are lost between two frames) and do not select frames according to the type of motion detected.

In this paper, we propose a new method of video summary based on camera motions (translation and zoom) or on static camera. We think that camera motion carries important information on video content. For example, a zoom in makes it possible to focus spectator attention on a particular event. In the same way, a translation indicates a change of place. Therefore, keyframes were selected according to camera motion characteristics. More precisely, the method consists in studying the succession and the magnitude of camera motions. From these two criteria, various rules are worked out to build the summary. For example, the keyframe selection will be different according to the magnitude and the succession of the motions detected. The advantage of this method is to avoid a direct comparison between frames (similarity measure or overlap between frames on pixel level) and it is based only on camera motion classification.

Video summarization methods must be evaluated to verify the relevance of the selected keyframes. As already mentioned, video summarization methods are widely studied in the literature. Nevertheless, there is no standard method to evaluate the various video summaries. Some authors [START_REF] Yahiaoui | Automatic video summarization[END_REF][START_REF] Ciocca | Dynamic key-frame extraction for video summarization[END_REF] propose objective (mathematical) measures that do not take human judgment into account. To overcome this problem, other authors propose subjective evaluation methods. Three families of subjective evaluation can be distinguished to judge video summarization methods.

The first family of methods compares two summaries. For example, in [START_REF] Corchs | Video summarization using a neurodynamical model of visual attention[END_REF], people view the entire video and choose between two summaries the one which best represents the video viewed. One summary results from a video summarization method to be tested and the other comes from another method developped by other researchers (a regular sampling of the video or a simplified version of the summarization method to be tested). The aim is to show that the summary suggested by one method is better than another method.

The second family creates a summary manually, a kind of "ground truth" of video, that is used for the comparison with the summary obtained by its automatic method. The comparison is made with some indices (recall and precision). The comparison is carried out either manually or by computing distances. For example, Ferman et al. [START_REF] Ferman | Two-stage hierarchical video summary extraction to match low-level user browsing preferences[END_REF] evaluate their summary by requiring a neutral observer to announce the forgotten keyframes and the redundant ones. The criteria of evaluation are thus the number of forgotten and redundant keyframes.

In the third family, subjects are asked to measure the level of meaning of the proposed summary. A subject views a video, then he is asked to judge the summary according to a given scale. Questions can also be asked the subjects to measure the degree of performance of the proposed summary. In [START_REF] Shao | A new approach to automatic music video summarization[END_REF], the quality of the summary is evaluated by asking subjects to give a mark between one and five for four criteria: clarity, conciseness, coherence and overall quality. In [START_REF] Ma | A user attention model for video summarization[END_REF], the subject must initially give an appreciation for each shot on the single selected keyframe (good, bad or neutral) then he must give appreciations on the number of keyframes per shot (good, too many, too few). In [START_REF] Lu | Video summarization by spatial-temporal graph optimization[END_REF], three questions are asked about the summary: who, what and coherence. Ngo et al. [START_REF] Ngo | Automatic video summarization by graph modeling[END_REF] propose two criteria of evaluation to judge the summary: informativeness and enjoyability. The first criterion reveals the ability of the summary to represent all the information in the video by avoiding redundancy, and the second evaluates the performance of the algorithm in giving enjoyable segments.

The evaluation method that we propose belongs to the second family. It consists in building an "optimal" summary, called the reference summary, from the summaries obtained by various subjects. Next, an automatic comparison is carried out between the reference summary and the summaries provided by various methods. This evaluation technique provides a method to test different summaries quickly.

The camera motion-based method to create a video summary is explained in Section 2. Then, in Section 3, the subjective method to evaluate the proposed summary is presented. Finally, Section 4 concludes the paper.

Video summarization method from camera motion

The principle of the summarization method consists in cutting up each video shot in segments of homogeneous camera motion, then in selecting the keyframes according to the succession and the magnitude of camera motions. The method requires the parameters extracted from the camera motion recognition and described in [START_REF] Guironnet | Camera motion classification based on transferable belief model[END_REF] to be known. A short recall of the camera motion recognition method is presented followed by an explanation of the keyframe selection method.

Recognition of camera motion

This recognition consists in detecting translation (pan and/or tilt), zoom and static camera in a video. The system architecture, depicted in figure 1, is made up of three phases: motion parameter extraction, camera motion classification (for example, zoom) and motion description (for example, zoom with an enlargement coefficient of five). The extraction phase consists in estimating the dominant motion between two successive frames by an affine parametric model. The core of the work is the classification phase which is based on Transferable Belief Model (TBM) and is divided into three stages.

The first stage is designed to convert the motion model parameters into symbolic values. This representation aims at facilitating the definition of rules to combine data and to provide frame-level "mass functions" for different camera motions. The second stage carries out a separation between static and dynamic (zoom, translation) frames. In the third stage, the temporal integration of motions is carried out. The advantage of this analysis is to preserve the motions with significant magnitude and duration. Finally, a motion is associated with each frame and a video is split into segments (i.e. set of successive frames having the same type of motion).

The description phase is then carried out by extracting different features on each video segment containing an identified camera motion type. For example, a zoom segment (Fig. 2.a) is represented by the enlargement coefficient ec and the direction of the zoom (in or out). A translation segment (Fig. 2.b) is described by the distance traveled noted dt and the total displacement noted td. The total displacement td corresponds to the displacement along the straight line between the initial and the final positions, whereas the distance traveled dt is the original path and corresponds to the integration of all displacements between sampling times.

Consequently, this method is used to identify and describe camera motion segments inside each video shot. The parameters extracted to describe translation and zoom segments will be used to create the summary.

Keyframe selection according to camera motions

Keyframe selection depends on camera motions in each video shot. As mentioned before, each shot is first cut into segments of homogenous camera motion. The keyframe selection is divided into two steps. First, some frames are chosen to be potential keyframes to describe each segment: one at the beginning and one at the end, and in some cases one in the middle. In practice, even for long segments, we noted that three keyframes are enough to describe each segment. Then, some of the keyframes are kept and others removed according to certain rules. We will present the keyframe selection first according to the succession of motions, second the magnitude of motions and finally by the combination of both.

Keyframe selection according to succession of camera motions

To select the keyframes, we define heuristic rules. Because of the compactness of the summary, only two frames are selected to describe the succession of two camera motions. If one of the two successive segments is static, the two frames are selected at the beginning and at the end of the segment with motion. One of these frames is also used to represent the static segment. If the two successive segments have camera motions, a frame is selected at the beginning of each segment. Figure 3 recapitulates how the keyframes are selected. The process is repeated iteratively for all the motion segments of the shot. This technique processes two consecutive motions at a time. Let us suppose that three consecutive motions are detected in a shot: static, translation and static. By applying the rules defined in figure 3, we obtain the results shown in figure 4. Each iteration corresponds to the process of two consecutive segments. By superposition of the iterations, the result obtained is two selected frames: one at the end of the static segment (or at the beginning of the translation segment) and one at the end of the translation segment (or at the beginning of the last segment). The first iteration corresponds to the process of segments 1 and 2. In the same way, the second iteration corresponds to the succession of segments 2 and 3. Keyframe selection is one frame at the end of the static segment (or beginning of the translation segment) and one frame at the end of the translation segment (or at the beginning of the last segment)

Keyframe selection according to magnitude of camera motions

Keyframe selection also has to take into account the magnitude of camera motions. For example, a translation motion with a strong magnitude requires more keyframes to be described than a static segment, since the visual content is more dissimilar from one frame to the following one. In the same way, a zoom segment is described by a number of keyframes linked to its enlargement coefficient.

For a translation segment, the coefficient c r = (dt -td)/dt is calculated in order to determine if the trajectory is rectilinear. This coefficient c r lies between 0 and 1 and describes the motion trajectory. The smaller c r is, the more rectilinear the motion is. Consequently, if coefficient c r is lower than a threshold δ r , the motion is considered rectilinear. In this case, if the total displacement td is large, i.e. higher than threshold δ td , the first and the last frames of the segment are selected. Only the last frame is selected if the total displacement td is weak (lower than threshold δ td ). On the other hand, if coefficient c r is higher than δ r , the motion changes direction. If the total displacement td is higher than threshold δ td , the frames of the beginning, the middle and the end of the segment are selected. If not, the last frame of the segment is selected.

For a zoom segment, the keyframes are selected according to the enlargement coefficient ec. If the enlargement is great (i.e. higher than threshold δ ec ), the first and the last frames of the segment are selected. In the opposite case, only the last frame is selected.

After an experimental study, we chose the following thresholds: δ r = 0.5, δ td = 300 and δ ec = 5. Keyframe selection according to camera motion magnitude is summarized in figure 5.

Keyframe selection according to succession and magnitude of camera motions

Keyframe selection takes into account both the succession and the magnitude of camera motions. We will combine the different rules explained above. First, the identified motions which have a weak magnitude or a weak duration are processed as static segments. If a translation motion of duration T with a total displacement td is detected, the standardized total displacement td s = td/T is calculated. This is regarded as a static segment if the duration T is shorter than threshold δ T and if the standardized total displacement td s is shorter than threshold δ t . In the same way, a zoom of duration T with an enlargement ec is regarded as a static segment if the duration T is shorter than threshold δ T and if the enlargement ec is lower than δ e . In our experiment, the thresholds were fixed in an empirical way at δ t = 1.5, δ e = 1.8 and δ T = 50.

Then, keyframes are selected by applying the rules according to the succession of motions. From the magnitude of motions, frames can be added for the summary. Let us have a look at the previous example with three consecutive detected motions in a shot: static, translation with a strong magnitude and static. Figure 6 illustrates the keyframe selection.

Moreover, in the case of a motion included in another one, if the motion included is of strong magnitude, then the segment containing this motion is described by the frame in the middle of this segment. Lastly, if a shot contains only one camera motion, then the keyframe selection is obtained by applying the rules according to the magnitude of the motions. 0 to frame 59) is identified as static and the keyframe corresponds to frame 29. In the same way, n°7 shot (from frame 378 to frame 503) contains two segments: a static segment (from frame 378 to frame 448) followed by a zoom segment (from frame 449 to frame 503). The keyframe selection for this shot are frames 413 and 503. Figure 7.c shows the keyframes used for the summary of the "Baseball" video.

For each shot of the "Baseball" video, the summary created from the succession and the magnitude of camera motions seems visually acceptable and presents little redundancy.

We developed a summary method which exploits the information provided by camera motion. In order to validate this method, we have designed an evaluation method.

Evaluation method of video summaries

Video summarization methods must be evaluated to verify the relevance of the selected keyframes. However, the quality of a video summary is based on subjective considerations. Only the "user" can judge the quality of a summary.

In this part, we propose a method to create an "optimal" summary based on summaries created by different people. This "optimal" summary, also called the reference summary, is used as a reference for the evaluation of the summaries provided by various approaches. The construction of a reference summary is a difficult stage which requires the intervention of subjects, but once this summary has been obtained, the comparison with another summary is rapid. Our evaluation method is similar to that of Huang et al. [START_REF] Huang | Automatic performance evaluation for video summarization[END_REF]. Nevertheless, although their evaluation occurs on the video level, their method of building the reference summary is carried out on the shot level. The evaluation method that we propose was developed within a more general framework and provides (i) a reference summary with keyframes selected per shot and (ii) a hierarchical reference summary that takes into account the "importance" of each shot to add weight to the keyframes of the corresponding shot. As the summary from camera motions is proposed on the shot level, we only present the evaluation method on the level of each shot. We will present successively the manual creation of a summary, then the creation of the reference summary and finally the comparison between the reference summary and the automatic summary provided by our camera motion-based method.

Creation of a video summary by a subject

The goal of the experiment is to design a summary for different videos. We asked subjects to watch a video then to create a summary manually. From the various summaries, a method is proposed to generate the reference summary in order to compare it with the summaries provided by various algorithms.

Video selection

Video selection is an important stage which can influence the results. Two criteria were taken into account: the content and the duration of the video. We chose three videos with varied content and different durations: a sports documentary (called "Documentary") with 20 shots and 3271 frames, "The Avengers" series with 27 shots and 2412 frames and TV news (called "TV news") with 42 shots and 6870 frames. Each video is made up of color frames (288x352 pixels) displayed at a frequency of 25 frames per second.

It should be noted that these videos are of short duration. The longest lasts approximately 5 minutes. In comparison, the longest video used in [START_REF] Huang | Automatic performance evaluation for video summarization[END_REF] has 3114 frames and has a maximum number of 20 shots. The fact of not choosing long videos is linked to the duration of annotation a subject. It is thus a question of finding a good compromise between a sufficient duration and a reasonable duration for the experiment. In our experiment, the manual creation of a video summary requires between 20 and 35 minutes.

Subjects

12 subjects participated in the experiment. They did the experiment three times (for the three videos). The order of video presentation is random from one subject to another. All the subjects had a normal or corrected to normal vision and they knew the aim of the experiment -the creation of a video summary -but they were not aware of our video summarization method based on camera motion.

Experimental design

The subjects did the experiment individually in front of a computer screen. The experiment is designed using a program written in C/C++ language. Each subject received the following instructions. On the one hand, the summary must be as short as possible and preserve the whole content. On the other hand, the summary must be as neutral as possible. It is thus the subject who distinguishes by himself the degree of acceptance of the summary. The creation of a video summary proceeds in three stages.

1 st stage : Viewing of the video

In the first stage, the subject viewed the whole video (frames and sound) then he had to give an oral summary in order to make sure that the video content was understood. He viewed the video a second time.

nd stage : Annotation of the video extracts

In the second stage, the video was viewed in the form of extracts presented in chronological order in the top left-hand corner of the screen (Fig. 8). Subject was asked to indicate the degree of importance of each extract. The extracts corresponded to successive shots of the video. They were presented to the subject as extracts and no information was given about the shots. Once the extract had been viewed, the subject specified the degree of importance by indicating if, according to him, this extract was "very important", "important" or "not important" for the summary of the video. The subject clicked on the corresponding notation in the top right-hand corner of the screen. Then, the subject was asked to choose frames to summarize the extract. In the bottom right-hand corner, the frames were presented according to a regular sampling (one frame out of ten). The subject had to select the frames which seemed to be the most representative of the shot (from at least one to three) bearing in mind that the selection had to be as concise as possible and represent the entirety of the content. The maximum number three was selected by preliminary tests. During this stage, when subjects were allowed to choose five keyframes, the majority of them chose fewer than three keyframes per shot, except for some of them who systematically chose five frames to describe even very short shots.

Once the subject had finished his annotation for a given extract, he validated it and the results were displayed in the bottom left-hand corner of the screen to keep a record of the annotations already given.

The second stage is illustrated in figure 8 ("Documentary" video). The subject indicated here if the extract was important for the summary of the video. He also selected one frame (frame n°2) to summarize this extract. The annotation of the previous extracts is displayed in the bottom left-hand corner where 5 frames were selected. Then in zone d, he had to select the frames which seemed relevant to him for the summary of the extract presented in zone a. As the frames were displayed with a spatial under-sampling by four, the subject could see them with a normal resolution by placing the mouse on a frame of zone d in order for it to appear in zone a. In zone c, the frames already selected from the preceding extracts were displayed to keep a record of the selection.

Two remarks can be made about this stage. The first concerns the limited number of levels of importance. Only three levels of importance are proposed: "very important", "important" or "no important". A scale with more levels would have made the task more complex and perhaps disconcerting for the subject because of the difficulty of making the difference between levels. The second is about the sampling of the frames of the extract. We chose the sampling of one frame out of ten to avoid displaying the complete shot on the screen, which would render the task of keyframe selection difficult and fastidious. Because of temporal redundancy of the frames, it seemed advisable to carry out this sampling and thus 5 frames displayed on the screen correspond to 2 seconds of the video.

3 rd stage: Confirmation of the annotations and construction of a short summary

In the third stage, once all the extracts had been annotated, the complete summary was displayed on the screen. The aim is to provide a global view of the summary and to allow the user to modify it and to validate it. Each extract was represented by the chosen frames and the degree of importance was indicated in the lower part of each frame. The subject was asked to modify, if he wished, the degree of importance of the extracts, then to remove the frames which appeared redundant and finally to select only a limited number of frames. The purpose of this stage is to provide a hierarchical summary with a fine level on a shot scale and a coarser level on the scale of the video.

In order to understand the experiment, a training phase is carried out with a test video with 5 shots and 477 frames.

Construction of a reference summary

The difficulty consists in creating a reference summary from the summaries created by various subjects. On the assumption that the summaries of subjects have a semantic significance, an "optimal" summary has to be built which takes into account these various summaries. Nevertheless, the differences between summaries are not measured by applying a distance between the frame descriptors since the gap between low level descriptors and semantic content

has not yet been bridged. The process is based on elementary considerations to create the optimal summary. We develop two methods to create a reference summary, one designed for each shot called "fine summary" and the other created from comparison between shots called "short summary". As the summary method from camera motions provides the keyframes for each shot, we only present the fine summary in this paper.

The construction of summary on the shot level is carried out only from the annotations of stage 2 . As already mentioned above, each extract viewed corresponds to a shot, and only the frames chosen by the subjects will be examined and not the degrees of importance of the shots. As the possible number of frames selected varies from one subject to another, the optimal number of keyframes must be given to represent an extract. The arithmetic mean could be used to determine the optimal number. Nevertheless, as the mean is influenced by atypical data, the median is privileged because of its robustness.

Once the number of keyframes has been found, it is necessary to determine how the frames chosen by the various subjects are distributed on a given level. Nevertheless, the temporal distribution of the frames is not enough, since it is not possible to take into account the temporal neighbourhood of frames. As frames were sampled one out of ten, two neighbouring frames can be selected by various subjects and can have the same content. Moreover, it is also necessary to differentiate the subjects who selected a few frames from those who selected many. According to the number of frames chosen by a subject for a given shot, a weight is given to each frame. If only one frame is selected for a given shot, the weight associated with the frame is worth three, whereas if three frames are chosen, the weight of each frame is equal to one. This strategy ensures an average weight by shot which is equal for each subject. This remains coherent with the fact that if a subject chose many frames, they would have a weak weight and inversely.

In order to take into account the neighbourhood of the selected frame, a gaussian, centered on the frame and with a standard deviation σ, is positioned according to a temporal axis. The magnitude of gaussian is according to the weight given above. If the subject chose for example only one frame to represent the shot, then only one gaussian was placed on the temporal axis with a magnitude of three. The standard deviation is an important parameter for the creation of the reference summary. The greater this parameter is, the more frames selected by the different subjects will be combined. Figure 9 shows how the weight of the close frames varies according to the parameter σ.

As the frames to be chosen were displayed according to a regular sampling, the weight of the close frame depends directly on this parameter and is located at index 10. For example, if σ = 20 then the weight of the close frame is worth 0.88. After accumulation of the answers, we obtain the temporal distribution of selected frames. Figure 10 shows the results for the "Documentary" sequence. We can note for example that the first shot is very long and has many local maxima whereas the second shot has one maximum. The maxima symbolizes the locations where the frames must be selected to summarize the video, since these locations are chosen by the subjects. We obtain the maxima by calculating the first derivative and by finding the changes of sign. They are sorted by decreasing order. The close local maxima are combined to avoid the presence of local maxima on a window lower than 2 seconds (or 50 frames). Moreover, all local maxima whose magnitude is lower than 20% of the global maximum are removed.

Finally, for each shot, we retained only the n first local maxima sorted by descending order according to the optimal number of frames required. They correspond to the keyframes selected to summarize the shot and thus the video.

The chosen parameter σ is explained with the description of our results. The maxima on this curve gives the selection of keyframes. The crosses on the curve are the frames chosen to summarize the video. The curve at the bottom corresponds to the staircase function between -0.5 and -1 that locates the changes of shot. In this example, the parameter σ is fixed at 20.

Comparison between the automatic summary and the reference summary

The comparison between the reference summary and the automatic summary obtained by an algorithm, called candidate summary, is a delicate task since it requires the comparison of frames. The process of comparison between the reference summary and the candidate summary for the shots is carried out in 4 stages. Figure 11 illustrates the comparison of the summaries for each shot. We can note in this example that the reference summary has 3 keyframes whereas the candidate summary has 4.

The first stage consists in determining the frames of the reference summary with which each frame of the candidate summary could be associated. Each candidate frame is thus associated if possible with two frames of the reference summary, which are temporally the closest frames in the same shot. For example, frame B of the candidate summary is associated with frames 1 and 2 of the reference summary (Fig. 11.a). On the other hand, frame A is only associated with frame 1, because it is the first frame of the shot.

The second stage consists in determining the most similar frame to the frame of the candidate summary among the two potential frames of the reference summary. For example, frame B which can be associated with either frame 1 or 2 is finally associated with frame 1 (Fig. 11.b) because it is assumed to be closer in terms of content. This requires the representation of frames by a descriptor and the definition of a distance between two frames. Nevertheless, it is difficult to compare the content of two frames. However, as the frames belong to the same shot, there is a temporal continuity between the frames and the comparison between the frames can be carried out by comparing their color histograms. Indeed, two similar histograms will have the same content since the frames are temporally continuous. Inside the same shot, the probability that two similar histograms correspond to different frame contents is very low.

The descriptor used here is a global color histogram obtained in color space YCbCr and the distance between histograms is obtained by the L1 norm. We chose not to present a color histogram, as it is not essential to understand the method. However, a detailed description can be found in [START_REF] Guironnet | Video classification based on low-level feature fusion model[END_REF].

The third stage deals with the case where several frames of the candidate summary are associated with the same frame of the reference summary. For example, frames A and B are associated with the same frame 1 (Fig. 11.b), and finally, only frame B is associated with frame 1 (Fig. 11.c) since the distance between frames 1 and B is assumed to be weaker.

Lastly, the fourth stage consists in preserving only the clustering where the distances are lower than a threshold δ s . The frames which were gathered can have great distances. Thresholding makes it possible to preserve only the frames gathered with similar content. The parameter δ s is fundamental and will be largely studied in the presentation of the results.

The comparison between the reference summary and the candidate summary leads to the number of frames gathered. The standard measures Precision (P), Recall (R) and F 1 (F 1 is a harmonic mean between Recall and Precision) can then be used to evaluate the candidate summary.

Evaluation of automatic summary

As the summary method from camera motion provides a shot level summary, we only study the evaluation method on the shot level. Five methods of creating summaries are tested: four are elementary summarization methods and one is our summarization method. For the first method, a number of keyframes is chosen randomly (between 1 and 3) for each shot, then the keyframes are chosen randomly (random summary). For the second method, keyframes are chosen randomly in each shot, but the number of keyframes is defined by the reference summary (semi-random summary). For the third method, only one keyframe is selected in the middle of each shot (center summary). For the fourth method, keyframes are selected with a regular sampling rate as a function of the shot length (one keyframe per 200 frames) (regular sampling summary). Finally, the last one is the one that we proposed using camera motion (camera motion-based summary).

It is important to note that the third method is classically used in the literature. The second one is, in practice, unfeasible. In fact the reference summary is not known, so the number of keyframes to be selected in each shot is unknown. This method might offer good candidate summaries, because they have the same number of keyframes as the reference one.

Table 1 recapitulates the evaluation of the five video summarization methods.

As we can see, the method that we propose according to the succession and the magnitude of motions provides the best results (in term of F 1 ) for the three videos. For the "Series" video, methods n°2, n°3 and n°4 present close results compared to the method according to the magnitude and the succession of motions. This confirms that the methods which select only one frame by shot (either a frame in the middle of the shot or at a random location in the shot) are relatively effective when the shots are of short duration. The "Series" video contains 16 shots out of 28 of less than 3 seconds whereas the "Documentary" and "TV News" video have respectively 8 shots out of 20 and 9 shots out of 42 of less than 3 seconds. It is indeed natural to select only one frame for these shots. However, the results for the three videos confirm the interest of using camera motion to select frames. The longer the shots are, the more likely the contents are to change and thus the more effective the method is. However, the comparison method of summaries requires various parameters to be fixed which can influence the results. In the method of reference summary construction, the parameter studied is the standard deviation of gaussian σ around the frame chosen by a subject. Indeed, if the parameter σ selected is low, then the close frames selected by the subjects cannot be combined. In the same way, if the parameter σ selected is large, then the frames will be gathered easily. Thus, the number of local maxima inside a shot depends on this parameter σ. Figure 12 illustrates the results of the summarization method with the keyframe selection in the center of the shot, and the method using succession and magnitude of motions according to parameter σ. Moreover, the results of the two methods presented remain relatively stable according to parameter σ. We can also note that the number of keyframes of the reference summary for the three videos does not decrease greatly with the increase of parameter σ. Thus, we can conclude that this parameter σ does not call into question the performance of the methods. Thereafter, this parameter σ will be fixed at 20.

Lastly, with regard to the comparison between the reference summary and the candidate summary, although the description of the frames is carried out by color histogram, clustering between frames is preserved only if the distances are lower than the threshold δ s . However, this threshold plays an important role in the results. Indeed, if the threshold selected is rather low, then the frames will be gathered with difficulty, whereas if the threshold is too large, the dissimilar frames can be matched together. Figure 13 illustrates the results of various methods according to threshold δ s . As expected, the more the threshold increases, the more the performances increase (up to a certain value). Nevertheless, whatever the threshold selected, the method according to the succession and the magnitude of motions presents the best results for the "Documentary" and "TV news" videos. With regard to the "Series" video, the most competitive method is that based on the magnitude and the succession of motions for thresholds 0.1, 0.2, 0.3 and 0.4. On the other hand, for thresholds 0.5 and 0.6, the summarization method with the frame in the center of the shot is more competitive. Generally, the performances obtained for thresholds 0.5 and 0.6 are fairly similar for the same video. That means that parameter δ s is too high and that dissimilar frames can be gathered. Parameter δ s should be selected inferior to 0.5 because the slope is nonnull.

Conclusion

In this paper, we have presented an original video summarization method from camera motion. It consists in selecting keyframes according to rules defined on the succession and the magnitude of camera motions. The rules we used are "natural" and aim to avoid temporal redundancy between frames and at the 21 same time to keep the whole content of the video. The camera motion brings "high level" information; in fact the camera motion is desired by the film maker and contains some cues about the action or an important location in a scene. The keyframe selection is directly based on the camera motion (succession and magnitude) and offers the advantage of not calculating differences between frames as it was done in other research.

A new evaluation method was also proposed to compare the different summaries created. A psychophysical experiment was set up to make it possible for a subject to create manually a summary for a given video. Twelve subjects summarized three different videos (duration from 1.5 to 5 minutes). A protocol was designed to combine these twelve summaries into a unique one for each video. This reference summary provided us with the "ideal" or "true" summary. Finally, we proposed an automatic comparison between this reference summary and the summary built by our method. This method can also be used to compare different kind of summaries, with different lengths.

One of the future lines of investigation would be to create what we previously called a hierarchical summary. This hierarchical summary would be based on our camera motion-based summary (per shot) and would include some criteria to measure the relative importance of each shot. This new criteria would be for example the magnitude of motion in a segment or for static segment, the relative "interest" of the segment. The relative interest can be described by a biological model of saliency. A "degree of importance" could be linked to each shot and the keyframes of the shot (selected by the camera motion) would be weighted with this index of "importance". A hierarchical summary can be easily evaluated with our subjective evaluation method. In fact, with this method, we already have access to the "important" information for each shot.

Stage 3 :Phase 2 : 2 : 1 : 3 : 1 :Figure 1 .

 3221311 Figure 1. System architecture for camera motion classification and description

Figure 2 .

 2 Figure 2. Example of parameters extracted to describe each segment of a video for a) a zoom and b) a translation.

Figure 3 .

 3 Figure 3. Rules for keyframe selection according to two consecutive camera motions.Cases: (a) translation and static, (b) zoom and static, (c) translation and zoom. For example, if a static segment is followed by a translation segment (figure (a) left), the first frame of the translation segment (or the last frame of the static segment) is selected as well as the last frame of the translation segment.

Figure 4 .

 4 Figure 4. Illustration of keyframe selection.The first iteration corresponds to the process of segments 1 and 2. In the same way, the second iteration corresponds to the succession of segments 2 and 3. Keyframe selection is one frame at the end of the static segment (or beginning of the translation segment) and one frame at the end of the translation segment (or at the beginning of the last segment)

Figure 5 .

 5 Figure 5. Keyframe selection according to the type and magnitude of camera motions

Figure 7 Figure 6 .

 76 Figure7illustrates the different steps of the summarization method proposed. It concerns a video sequence named "Baseball", an extract from a baseball match, which has 9 shots (Fig.7.a). In figure7.b, from the bottom upwards on the y-axis, we have respectively the position of the shots, the identification of static segment (absence of motion), translation segment and zoom segment, and finally the selection of the keyframes. For example, n°1 shot (from frame

Figure 7 .

 7 Figure 7. Example of video summary made by camera motion-based method .

Figure 8 .

 8 Figure 8. Second stage of the reference summary creation for the "Documentary" video. The subject had to indicate the degree of importance of the extract in zone b. Then in zone d, he had to select the frames which seemed relevant to him for the summary of the extract presented in zone a. As the frames were displayed with a spatial under-sampling by four, the subject could see them with a normal resolution by placing the mouse on a frame of zone d in order for it to appear in zone a. In zone c, the frames already selected from the preceding extracts were displayed to keep a record of the selection.

Figure 9 .

 9 Figure 9. Parameter σ according to the frame chosen by the subject. The gaussian is positioned on the selected frame. For example, if the parameter σ = 10 then the close frame (on the left or on the right) has a weight of 0.6 and the following frame has a weight of 0.13, since the frames are displayed according to a regular sampling (all ten).

Figure 10 .

 10 Figure 10. Distribution of keyframe selection on the "Documentary" video standardized by the number of subjects (horizontal axis corresponds to the frame number). The maxima on this curve gives the selection of keyframes. The crosses on the curve are the frames chosen to summarize the video. The curve at the bottom corresponds to the staircase function between -0.5 and -1 that locates the changes of shot. In this example, the parameter σ is fixed at 20.

Figure 11 .

 11 Figure 11. Illustration of the comparison for each shot between the reference summary and the candidate summary. The reference summary has 3 frames (from 1 to 3) whereas the candidate summary presents 4 frames (of A with D). (a), (b) and (c) represent the first three stages of the comparison.

Figure 12 .

 12 Figure 12. F 1 as a function of the parameter σ for two summarization methods (summaries by selecting the center of each shot and based on camera motion) for three videos. The threshold δ s is fixed at 0.3. The third curve, at the bottom of each subfigure, corresponds to the number of keyframes for the reference summary as a function of the parameter σ.

Figure 13 .

 13 Figure 13. F 1 as a function of the parameter δ s for four summarization methods and for the three videos. The parameter σ is fixed at 20.

Table 1

 1 Results of the four summarization methods for the three videos. The threshold δ s of clustering between two frames is fixed at 0.3 and the parameter σ is 20 (R: Recall, P : Precision, F 1 ). n°1: random summary, n°2: semi-random summary, n°3: summary by selecting the frame in the center of each shot, n°4 summary based on a regular sampling and n°5 summary based on camera motion

	Sum-		Documentary			TV News			Series
	mary	R	P	F 1	R	P	F 1	R	P	F 1
	n°1	62 (15/24) 40 (15/37) 49.1 83 (46/55) 50 (46/91) 63.0 80 (24/30) 40 (24/59) 53.9
	n°2	54 (13/24) 54 (13/24) 54.1 72 (40/55) 72 (40/55) 72.7 76 (23/30) 76 (23/30) 76.6
	n°3	50 (12/24) 60 (12/20) 54.5 63 (35/55) 83 (35/42) 72.1 73 (22/30) 78 (22/28) 75.8
	n°4	62 (15/24) 54 (15/28) 57.7 69 (38/55) 70 (38/54) 69.7 73 (22/30) 73 (22/30) 73.3
	n°5	79 (19/24) 55 (19/34) 65.5 80 (44/55) 77 (44/57) 78.5 86 (26/30) 72 (26/36) 78.7

Acknowledgment

The authors would like to thank C. Marendaz and D. Alleyson (Laboratoire de Psychologie et NeuroCognition, Grenoble, France) for having welcomed us and helped us with the experiments. We also would like to thank S. Marat for her help in testing some summarization methods.