Philippe Mulhem
email: mulhem@i2r.a-star.edu.sg

Joo-Hwee Lim

Home Photo Retrieval: Time matters

Temporal information has been regarded as a key vehicle for sorting and grouping home photos into albums associated with events. While time-based browsing might be adequate for relatively small photo collection, query and retrieval would be very useful to find relevant photos of an event in large collection. In this paper, we propose the use of temporal events for organizing and representing home photos using structured document formalism and hence a new way to retrieve photos of an event using both image content and temporal context. We describe a hierarchical model of temporal events and the algorithm to construct it from a collection of home photos. In particular, we compute metadata of a node from the metadata of its children recursively to facilitate content-based and context-based matching between a query and an event. With semantic content representation extracted using Visual Keywords and Extended Conceptual Graphs, we demonstrate the effectiveness of photo retrieval on 2400 timestamped heterogeneous home photos with very promising results.

Introduction

It had been shown [START_REF] Rodden | How do people organise their photograpghs[END_REF][START_REF] Rodden | How People Manage Their Digital Photos? ACM CHI[END_REF] that time is the most prominent aspect used by consumers to retrieve their photos: when they want to retrieve photos of one event, they consider "I roughly know when it is […]" (p. 5 of [START_REF] Rodden | How People Manage Their Digital Photos? ACM CHI[END_REF]) and they browse their collections until they found what they are looking for. Indeed temporal information has been utilized for sorting and grouping home photos into albums associated with events, which is the most requested feature for photo organization [START_REF] Rodden | How do people organise their photograpghs[END_REF].

For instance, MyPhotos [START_REF] Sun | MyPhotos -A System for Home Management and Processing[END_REF] proposes effective temporal navigation in photo collections. The Hierarchical Browser described in [START_REF] Graham | Time as an Essence for Photo Browsing Through Personal Digital Libraries[END_REF] exploited photo creation time to cluster photos and to generate meaningful summaries. The PhotoTOC [START_REF] Platt | PhotoTOC: Automatic Clustering for Browsing Personal Photographs[END_REF] and AutoAlbum [START_REF] Platt | AutoAlbum: Clustering Digital Photographs Using Probabilistic Model Merging[END_REF] used adaptive gap threshold to split photos sorted by creation time into albums and a left-right Hidden Markov Model on the color information to further cluster the ordered photos in large albums. These works share a common insight and assumption: the irregular and recursive busty time patterns of home photos taking correlate well with events [START_REF] Graham | Time as an Essence for Photo Browsing Through Personal Digital Libraries[END_REF] and a noticeable time gap correspond to a change in event [START_REF] Platt | PhotoTOC: Automatic Clustering for Browsing Personal Photographs[END_REF].

While time-based browsing might be adequate for relatively small photo collection (average of 1000 photos in [START_REF] Rodden | How People Manage Their Digital Photos? ACM CHI[END_REF]; 1300 or less photos in [START_REF] Platt | PhotoTOC: Automatic Clustering for Browsing Personal Photographs[END_REF]), query and retrieval would be very useful to find relevant photos of an event in large collection. In this paper, we propose the use of temporal events for organizing and representing home photos using structured document formalism and hence a new way to retrieve photos of an event using both image content and temporal context.

Though time-based photo clusters are good and sensible approximation to photo events, we remind that in some situations, the time gap may not reflect the true semantic boundary of an event. Hence we prefer to use the term "temporal event" to reflect the approximation in this paper. Furthermore, we draw the analogy of a home photo collection to a structured document. Images grouped into temporal events are indeed considered similar in some way to parts in a structured document as shown in Fig. 1. The images at the leaf nodes correspond to passages in a structured document.

Fig. 1. A typical temporal organization of a home photo collection

For such documents, several approaches try to use the explicit structure to provide clues to retrieve the best document parts that match the queries: [START_REF] Chiaramella | A Model for Multimedia Information Retrieval[END_REF] for the FERMI Esprit II project defined a logical-based expression of information propagation and query processing. This work was used in [START_REF] Lalmas | Dempster-Shafer's Theory of Evidence Applied to Structured Documents Modelling Uncertainty[END_REF] to provide a Dempster-Shafer frameword for structured document retrieval. Other approaches use the structure to facilitate the computation of relevance status values, but not for the selection of the level of structure retrieved: the work reported in [START_REF] Wilkinson | Effective Retrieval of Structured Documents[END_REF] employed the usual vector space model to index whole documents and document parts and to retrieve document parts, showing that the context of document parts may be used to retrieve relevant chapters of legal documents; [START_REF] Myaeng | A Flexible Model for Retrieval of SGML Documents[END_REF] use probabilistic belief networks to represent the impact of retrieval of the structure of documents. Our approach is more related to the latter approaches: we study the impact of the temporal structure on image retrieval.

From another point of view, some works have considered different uses of links in, or between, web or hypertext pages. [START_REF] Marchiori | The Quest for Correct Information on the Web: Hyper Search Engines[END_REF] defined a notion of "local neighborhood" of a web page that is used to compute the relevance of a page using the out-going links to provide better results than usual non-contextual searches. [START_REF] Craswell | Effective site finding using link anchor information[END_REF] shown that the links between web pages are important to find out what are the "best" entries of the entire web sites. [START_REF] Dunlop | Multimedia Information Retrieval[END_REF] used the links of texts nodes connected to image nodes in hypertext documents to index images. [START_REF] Chang | Searching for Images and Videos on the World-Wide Web[END_REF] uses the context of occurrence of images or video links to classify them. We adopt similar belief that Collection

T_Event 1 … T_Event n T_Event 1,1 …. T_Event 1,n1 … T_Event n,1 … T_Event n,nn Image 1 … Image i -3 -
the composition of temporal events may be used as contexts during the query processing of image retrieval. We organize the rest of the paper as follows. We describe a hierarchical model of temporal events and the algorithm to construct it from a collection of home photos in the next section. The metadata definition of a temporal event based on the metadata of its children as well as content-based and context-based query processing is presented in Section 3. We demonstrate the effectiveness of photo retrieval on 2400 time-stamped heterogeneous home photos with very promising results in Section 4 followed by conclusion.

Temporal Events Modeling

A temporal event T ∈ T, on a collection of image I is a triplet (Sub-T, Image, Index) where Sub-T (⊂ T) is the set of temporal events that compose T, Image (⊂ I) is the set of images that compose T, and Index is the content representation of T. We ensure that a temporal event T is composed of either images or sub-events but not both i.e. T.Sub-T XOR T.Image = ∅ where a dot notation A.B indicates the access to the element B of the instance A.

An image Im is described by a triplet (Image_Data, Time, Index), with Image_Data being the raw pixel data of the image, Time as time and date of image creation, Index as the metadata describing the content of the image. We define F images as a mapping from T to I that contains the list of all the images (directly or transitively) belonged to a temporal event.

Using the F images function, we formalize the fact that a temporal event contains only consecutive images:

)) . , . , . between( ) ( \ I ( ), ( ,

3 2 1 3 2 1 Time I Time I Time I T F I T F I I images images ∈ ∃ ¬ ∈ ∀
where between denotes a ternary predicate that is true if the time symbol in the third place is between the other two time symbols.

To build a hierarchy of temporal events, we use an approach inspired from the single-link clustering [5, p.233]. For level of granularity G k , we compute the matrix storing the image×image temporal differences. Then we use a one-pass single link algorithm to build the temporal events using these temporal distances and a threshold over which the images are not linked. We denote I Gk the set of initial temporal events for the granularity G k . A nice property of this approach is that for two granularities G i and G j with respective thresholds Thgi and Th gj , Th gi < Th gj , we ensure that for each initial temporal event T l of T Gi the images of T l are strictly included in the images of one and only one initial temporal event T m of T Gj . This is very similar to that of PhotoTOC [START_REF] Platt | PhotoTOC: Automatic Clustering for Browsing Personal Photographs[END_REF] and the first step of the clustering in [START_REF] Graham | Time as an Essence for Photo Browsing Through Personal Digital Libraries[END_REF]. In implementation, the difference between Gregorian date and time are computed with the use of Julian day numbers. For example, consider a collection I of 6 images with a DDMMYYYY time format,

I = {Im 1 [15081998], Im 2 [16081998], Im 3 [19081998], Im 4 [20081998], Im 5 [27081998], Im 6 [28081998]}.
A first generation of initial temporal events T G1 for a threshold of one day is {{Im 1 , Im 2 }, { Im 3 , Im 4 }, {Im 5 , Im 6 }}. The generation T G5 of a threshold of 5 days is {{Im 1 , Im 2 , Im 3 , Im 4 }, {Im 5 , Im 6 }}. In this paper, we decided to limit the temporal definition using temporal features, to be able in the future do study the impact of other ways (like imag content) to define such events.

Content Representation and Query Processing

The content representation (or index) of a temporal event is based on the content representation of its composing images. For a temporal event T, we define T.Index = F index ({im.time,im.index} | im ∈ F images (T)}) where the function F index computes the index of a temporal event based on the time and indexes of the images that belong to it. In this paper, the index associated with an image I, denoted as (I.Index vk , I.Index cg ), is composed of both a Visual Keywords representation Index vk and an Extended Conceptual Graph representation Index cg (please refer to [START_REF] Mulhem | Symbolic photo content-based retrieval[END_REF] for details). We compare in this paper two simple definitions of the function F index :

• The index of a temporal event is that of the image with the highest burst rate in the temporal event suggesting that this photo is important (as mentioned in [START_REF] Rodden | How People Manage Their Digital Photos? ACM CHI[END_REF], people take several digital photos to ensure they obtain a good one); • The index of the temporal event is considered as a simple concatenation of the indexes of the images that belong to the event;

Image Index based on Visual Keywords

Visual Keywords are local semantic regions derived from statistical learning (e.g. Support Vector Machines). Fig. 2 shows the 26 classes of Visual Keywords defined and learned for the 2400 home photos in our experiments. For the purpose of image indexing, multi-scale view-based recognition against these 26 Visual Keywords are performed on each image and the probabilistic recognition results are reconciled upon multiple resolutions and aggregated according to configurable spatial tessellation (e.g. five coarse areas: left, right, top, bottom, center) as the image index. In essence, an image area is represented as a histogram of Visual Keywords based on the certainties of local recognition. The similarity matching function beween a query Q vk represented by the index of a query image and the index of a image I of the database, Match VK (Q vk , I.Index vk ), is simply a weighted similarity between the corresponding areas in the query and the image (for example, we can assign higher weight for the center area as consumers tend to place the subject at the center while taking pictures). The similarity between two images areas is a simple histogram city-block distance computed between the visual keywords histograms in which the bins now possess semantic meanings. The choice of visual keywords is specific to a given context, because it's well known that the current state of the art of computer vision and image understanding is far from being able to provide accurate results in any context. So, the Visual Keywords are defined a priori and each Visual Keyword specificity is learned using examples, and each photo of the collection is then labelled automatically without any human intervention.

Image Index based on Extended Conceptual Graphs

The description of the images using Extended Conceptual Graphs is achieved through the use of a knowledge representation formalism, namely the Conceptual Graphs [START_REF] Sowa | Conceptual Structures: Information Processing in Mind and Machine[END_REF]. Because of space constraints, we only present an example in Fig. 3, where the left part presents a photograph and the right part an excerpt of the conceptual graph that index this image. Such conceptual graph representation is based on the VK labeling process decribed above, but the advantage of using graphs if the possiblity of simulating deduction. Compared to the initial definition of conceptual graphs by Sowa, we use additional elements dedicated to represent the certainty of recognition of elements (for instance, the Mountain concept of Fig. 3 is recognized with a certainty of 0.5), and the importance of the elements (for instance the Sky concept of Fig. 3 has an importance of 0.32). The query processing for the conceptual graphs is related to the function Match CG (Q cg , I.Index cg ), and based on sub-graph matching (the query Q cg being also a conceptual graph), considering the importance and certainty of recognition of the elements of the image index I.Index cg and the query. The query Q cg may be an image index, as in the experiments conducted in Section 4. The matching value uses the concepts weights as well as relationships weights.

Selection-based Temporal Event Index

The first option, in order to generate the index of a temporal event, is to select one image, hopefully the most representative, and to use its index as the index of the temporal event.

To integrate the use of such temporal aspects when finding the most important image of a temporal event, we define the function f k , from N + (going from the time and date of the first image of the event, in seconds, to the time and date of the last image of the event) to N + (image numbers going from 1 to y), as an interpolation of the cumulative function indicating for one time point t x the number of images taken before (or at) t x in the event. Such a function f k is generated by using cubic spline interpolation [START_REF] Press | Cubic Spline Interpolation, Sub-Chapter 3.3 of Numerical Recipes in C -The Art of Scientific Computing[END_REF]. The temporal representative value of an image p is then computed as the absolute value of the derivative f' k =dfk/dt for the value t p .

The index of a temporal event T is then similar to that of an image i.e. (T.Index vk , T.Index cg ). The query processing is computed using a matching function Match TE defined as a linear combination of the Match cg and Match vk of the query and the representative image:

Match TE (Q,T.Index) = α*Match(Q vk ,T.Index vk ) + (1-α)*Match(Q cg ,T.Index cg )

Aggregation-based Temporal Event Index

If one is not convinced that a selected image is always adequate to represent a temporal event effectively for different queries, then as a second approach we represent the index of a temporal event as the union of the indexes of its images, so T.Index is a set of couples (c.Index vk , c.Index cg ), one c per image of T. Query processing is based on the maximum matching (i.e. best match) obtained between the query and the indexes: Match TE (Q,T.Index)= max {α*Match(Q vk ,c.Index vk ) + (1-α)*Match(Q cg ,c.Index cg )} c∈T.Index

Query Processing for Temporal Events

The query processing for a temporal event is analogous to that for text passage retrieval [START_REF] Wilkinson | Effective Retrieval of Structured Documents[END_REF] with a query image being a passage and a temporal event as a document part. The retrieval function performed for a query Q and one image I depends on 3 representations: the query representation, the index of I, and the indexes of the temporal events that contain I. As we described earlier, in our case the query Q, the image index, and temporal event index are based on both VK and CG representations. Using the temporal event indexes during query processing, we intend to capture both the relevance of an image per se and the relevance of temporal context. We assign the importance of a temporal event as inversely proportional to its duration for query processing. Hence we compute the relevance status value of an image Im for a query Q as

RSV(Q,I) = α*Match vk (Q vk , I.Index vk ) + (1-α)*Match cg (Q cg ,I.Index cg ) + ∑ β(T,I) * Match TE (Q,T.Index) T ∈ T s.t. I ∈ F images (T) -7 -
where β is a function that returns the value of importance of a temporal event based on its duration and on the structural distance between the temporal event T and I, assuming that the longer an event is, the lower it representents accurately one image, and that the further the event from the image in the structure representation the lower in impacts the image retrieval.

Experiments on Image Retrieval by Content and Context

The experiments were conducted on a set I of 2400 home photos with temporal metadata year, month, day, hour, minute, and second. The temporal events were built as described in Section 2, with a granularity level of 1 hour: photos taken within an one-hour interval are assumed to be related to a short event. Thus in this paper, we only consider one level of temporal events that plays important role as temporal context. The number of temporal events obtained is 278, giving an average of 8 photos per event, with a maximum of 90 images and a minimum of 1 image. The β function is a linear function that only considers 1) temporal events duration within a day (i.e. 86400 seconds), assuming the existence of the function duration(T) gives the duration (in seconds) of the event and 2) the inverse of the distance (Str_distance) between the event and one image in the events structure: The experiments were conducted on a set of 26 queries, using query by example, with the VK (c.f. 3.1) and CG (c.f. 3.2) representations, using α equal to 0.9. The assesment of the relevant images for each query has been made by two persons, and we kept the intersection of the two assessments. The queries correspond to different runs of the system to retrieve images corresponding to meals, weddings, parks, watersides and beaches, swimming pool, streets and roadsides photos. The images of the query by examples were selected randomly from the relevant set for each query. We present the recall versus precision1 curves (Fig. 4), averaged over the 26 queries, obtained with and without the use of the temporal events using our approach (denoted as SYMB) and we compare as a baseline to a color-only approach using a 4x4 grid of HSV color-space local histograms (similar to the PicHunter System [START_REF] Cox | The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments[END_REF], denoted as HSV). We study the use of the representative image (c.f. 3.3, denoted as Rep) and the use of the whole set of image indexes (c.f. 3.4, denoted as Max).

     > ≤ - ⋅ = 86400 ) ( if 0 86400 ) ( if ) 86400 ) ( 1 ( ) , ( 1 )
In Fig. 4, we see that the use of both representative-based or max-based temporal events increases the quality of the results obtained: +5.6% for Rep SYMB compared to SYMB, +6.9% for Rep HSV, but the most convincing results are +25% for Max SYMB as well as for Max HSV. This clearly shows that the use of contextual information for the retrieval of home photos impacts positively each of the content representations. It also shows that the use of the representative image (Rep) does not perform as well as the Max, as expected. We also confirm that our use of symbolic representations surpasses the low-level color-based representation scheme. We notice also that the Max HSV scheme (average precision 0.3178) performs almost as good as the basic SYMB scheme (average precision of 0.3253), wich reinforce our idea that the context is important when considering image retrieval. Fig. 4. Recall Precision with and without using the temporal events.

Table 1 presents average precision values at top 5, 10, 20 and 30 photos for the 26 queries. The values in parentheses indicate the relative increase. This table highlights the importance of the precision improvement when presenting results to the user. For instance, at top 10 photos, the average number of relevant photos is 0.65 for SYMB and 0.8 for Max SYMB (meaning that there is on average 8 relevant photos among the first 10 retrieved), and 0.4 for HSV and 0.65 (on average 6.5 relevant photos in the first 10 retrieved). At top 30 photos, the number of relevant documents is on average 15 for SYMB and 20 for Max Symb, and also 10 for HSV and 15.9 for Max HSV; the precision of Max SYMB at top 30 photos is greater than 2/3, empirical threshold under which we consider such system to be unusable by consumers. We notice once again that the Max HSV using the event infomartion performs as well as the basic SYMB sheme.

Conclusion

In this paper, we have proposed a structured document formalism of temporal events for organizing and representing home photos. We defined how to create the temporal events from a collection of temporally tagged home photos, and we described several ways to create the index corresponding to these temporal events. We formalized and developed a new way to retrieve photos using both image content and temporal context (the temporal events) and demonstrated its effectiveness and practicality for photo retrieval on a collection of 2400 home photos using 26 queries by example.

The results obtained are very promising, and we will consider in future works the retrieval of photos belonged to an entire event. Other future directions will focus on other approaches to find one or more representative image of events, in a way to avoid the use of all the images to represent the index of each temporal event.

Fig. 2 .Fig. 3 .

 23 Fig. 2. The 26 Visual Keywords adopted for home photos.

Table 1 . Average precision values at 5, 10, 20 and 30 images.

 1

	Avg. Prec.	SYMB	Max SYMB	HSV	Max HSV
	@ 5 photos	0.731	0.88 (+20.4%)	0.508	0.72 (+41.8%)
	@ 10 photos	0.658	0.80 (+21.6%)	0.400	0.65 (+62.5%)
	@ 20 photos	0.552	0.73 (+31.5%)	0.348	0.58 (+67.2%)
	@ 30 photos	0.506	0.67 (+32.7%)	0.341	0.53 (+55.2%)

Using the trec_eval software from ftp://ftp.cs.cornell.edu/pub/smart/ .


Anne Guérin-Dugué

Stéphane Ayache
email: stephane.ayache@imag.fr

Catherine Berrut
email: catherine.berrut@imag.fr

Image retrieval : a first step for a human centered approach 1

Keywords: Content analysis, Image retrieval, Perception, User design, Relevance feedback

Image indexing using content analysis is known as a difficult task, involving the vision research domain. Using these tools in the context of a retrieval system is generally frustrating for users, due to a lack of interfaces development, and to the difficulty for users to understand the low-level features managed by the system. We propose in this paper a general point of view for introducing a link between such systems and potential users. This includes image features based on visual perception models, a relevance feedback model, and a graphical interface to express the information need through user-system interactions.

Introduction

The goal of content-based image retrieval (CBIR) systems is to help users in finding images among wide image collections. Actually, such systems have a double goal: building a system able to help users, but also characterizing the content of images such that they could be retrieved by the system. This double goal is known to be antagonistic because on one hand, the system computes the similarities between images from low level features, and on the other hand, the user looks for images with his own interpretation and motivation, which are mostly and unconsciously semantically driven.

The domain of image characterization aims at automatically extracting features (color, texture, shape, appearance, etc.). This is a very challenging task in many Computer Science and Vision laboratories. During the past decade, a number of systems have been developed, showing an intensive research activity : [START_REF] Bimbo | Visual Information Retrieval[END_REF][START_REF] Smeulders | Content-Based Image Retrieval at the End of the Early Years[END_REF] give firstly a good review of the domain and of existing systems, and secondly future trends.

However, the qualitative performance of systems is bounded by a semantic gap because these systems are mainly based on low-level features. To bridge this gap, some systems have been provided with "relevance feedback" [3, for example] derived from the information retrieval domain [START_REF] Rocchio | Relevance feedback information retrieval[END_REF]. Upstream, there exist experimental studies on perceptual similarities, in order to capture the semantic meaning of the images, and to understand the main visual categories driving visual perception [START_REF] Rogowitz | Perceptual Image Similarity Experiments, Vision and Electronic Imaging III[END_REF]. In fact, these studies imply perceptive metrics to be integrated in such systems [START_REF] Mojsilovic | Capturing image semantics with lowlevel descriptors[END_REF][START_REF] Vailaya | On image classification : City images vs Landscapes[END_REF]. Statistical models are proposed to get benefit of low level features distribution in natural images, for visual categories [START_REF] Torralba | Statistics of natural image categories[END_REF]. Downstream, protocols already established in the Information Retrieval domain are used in experimental evaluations [START_REF] Salton | Introduction to modern information retrieval[END_REF]. This work is developed in the framework of the SCOPIE project whose objectives are the design and conception of a user centered CBIR system, according to three axis, (i) image characterization through visual perception models, (ii) validation by psychological experimentations, and (iii) framework for user interactions. This article is focused on this last issue, developed into two points, firstly on the interface for interaction and secondly on a non parametric model of relevance.

Our analysis

As far as the system understands low-level features, and the user generally makes a correlation between relevant documents and a semantic need, the misunderstanding between the user and the system exists and generally emphasized with the usage of relevance feedback.

Our analysis of existing systems leads to several criticisms :

Indexing with features, initially developed for another context in computer vision, to a user front-end system, such as an image retrieval system ; Querying : Most systems interact from a QBE (query by example) interface with a query-answer-query-answer-… dialog, with a ranked list of retrieved images. In such a context, the user has to query and query without any help in the global session he is having.

Relevance feedback : Generally systems introducing feedback use the idea issued from the information retrieval domain. The drawback of this calculus stands in its uniform view of the relevant and not-relevant documents, and does not take into account subsets which could emphasize individual needs. Even if there exist systems which provide feedback through each indexing modality, the individual way of choosing an image should be more taken into account.

Our point of view

Our global position related to the state of the art consists in emphasizing the human needs and expression possibilities. Such approaches are rare, and very limited in existing systems We underline that efforts should be made in order to make the user having priority in the development and analysis of such systems. This could be introduced at different levels:

Indexing : The use of features based on visual perception models in the indexing process. Managing the images with these algorithms provides at least a way of interpreting images such as a user would perceive. The challenge here consists in providing a more intuitive images ranking according to perceptual dimension giving a global semantic interpretation : for example, artificial/natural scenes, indoor/outdoor scenes, … In [START_REF] Oliva | Modeling the Shape of the Scene : A Holistic Representation of the Spatial Envelope[END_REF], the authors show how perceptual properties of natural images (naturaless, openness, roughness, ruggedness and expansion) are correlated with global and local spectral information in the scene. From a projection on these perceptual dimensions, the semantic of the scene can be inferred without segmentation and object identification.

Querying :

The development of a user dedicated workspace: the gap system/user exists. Since this is our reality, we must provide to the user a double level of expression: querying a system, AND/OR a workspace for the management of his retrieval. In such a context, there is an evident need for the development of tools dedicating to the management of the user session. Very interesting approaches has been developed in [START_REF] Santini | A User Interface for Emergent Semantics in Image Databases[END_REF] where the interactions to extract semantic are developed through a similarity based model. Here, we use the "ostensive model" developed in [START_REF] Campbell | Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments[END_REF] for Information Retrieval and our aim is to generalize this model in the context of an CBIR system. In this model, the user doesn't explicitly formulate a query : he just expresses his information need through a relevance feedback. The progressive development of the information need is integrated inside a session.

Relevance feedback : Adaptation of the system to every user by creating retro-analysis of the user's interaction is needed in order to individually adapt the system. We first propose to study the feedback inside each indexing modality : this should emphasize each user points of interest within each modality. For example, let us suppose that a user is interested by a special landscape in autumn and winter. Within the chrominance modality, there will roughly exist two separate sets of relevant documents : the white ones, and the orangebrown ones. Classical feedback would minimize the weight of this modality due to the large within-distance between all the relevant documents. We propose to model the relevance and the non-relevance of the documents with a non parametric model inspired from [START_REF] Laaksonen | PicSOM : Self-Organizing Maps for Content-Based Image Retrieval[END_REF] in which no assumption is considered about the distribution of the relevance and non relevance documents. We also propose to analyze feedback within the global session, in order to study it as the real expression of the user's need. Going on with evaluations by down (classical system evaluation like in information retrieval) and upstream (user data on perceptual similarities) experimentations, which guarantee a good system-user interaction. Going back to the ostensive model where the temporal dimension is introduced, we develop the relevance feedback in this model which is entirely based on the reformulation.

Low level perceptual features

This CBIR system uses classical low level features, color and texture, through a visual perception point of view.

Color representation

For image retrieval systems, color information is one of the most important features. We propose a perceptive color space based on a biological model of the retina inducing a kind of color-constancy behavior [START_REF] Alleysson | Variability in color discrimination data explained by a generic model of retina with non linear and adaptative processing[END_REF]. In the retina of man and primates, the three kinds of photoreceptors are known to present a non-linear and adaptive transduction function. If C i (x, y) is the image of the i-th colour component activating the photoreceptors of class i, the response of this class is a compressed image :

c i (x, y) = C i (x, y) C i (x, y) + h(x, y) * C i (x, y) ,
(1) where the term h(x, y) * C i (x, y) is a kind of local mean of the input image C i (x, y) ( , obtained by the convolution with a normalised low-pass kernel: h x, y) . The result is that (i) each photoreceptor produces an output image which is more compressed in highlights, and (ii) the resulting colour of each pixel is affected by the local surrounding colour of the scene, i.e. mainly the colour of illumination. Then if we name R(x, y) , G(x, y) and B(x, y) the "stimulation color space", we can name the compressed signals r (x, y) , g( x, y) and b(x, y) l c = r( the "perceptive color space" from [START_REF] Bimbo | Visual Information Retrieval[END_REF]

a c = r(x, y) -g(x, y) c
, . These three signals define the new perceptive compressed color space with the corresponding luminance ( l ) and chrominance ( a , b ) components. The chrominance modes of the images are well enhanced allowing an efficient parsimonious statistical model by a 2D Gaussian mixture where the number of modes is automatically selected. Visual dissimilarities by comparing two chromatic distributions, are implemented through the Kullback-Leibler divergence. Spatial information is introduced after segmentation and then the spatio-chromatic distribution is derived. The spatio-chromatic dissimilarity is naturally deduced in the same way. [START_REF] Guérin-Dugué | Statistical modelling of a perception based colour representation for image characterisation and retrieval[END_REF] summarizes this approach and presents numerical experiments where this method using this new color space is advantageously compared with classical color spaces like Lab and CbCr.

Luminance information by "Independent Component Analysis"

Low level features are directly extracted from images with Independent Component Analysis (ICA) [START_REF] Hyvärinen | Independent Component Analysis[END_REF]. When ICA is applied to a set of natural images, it provides band-passoriented filters, similar to simple cells of the primary visual cortex [START_REF] Bell | The Independent Components of Natural Scenes are Edge Filter[END_REF]. These filters compose a new basis function set in which images are encoded by independent features. Moreover, the property of independence allows us to use a more adapted dissimilarity function than the classic norm based distance : it provides a justified way to estimate the Kullback-Leibler divergence between two images by the sum of the divergences between the marginal densities of both [START_REF] Borgne | Classification of images with independent features[END_REF]. Figure 1 illustrates the process with one filter belonging to the filters set extracted by ICA. One hundred and twenty filters are extracted from 32×32 patches of a learning images database. For each image and each filter, the histogram of the energy values inside logarithmically distributed bins compose the signature (33 bins) from which the dissimilarity is processed in the same Kullback-Leibler divergence framework. See [START_REF] Borgne | Classification of images with independent features[END_REF] for details.

Information for browsing

The choice here is to use the same coherent approach for all the features : model the distribution with a selected method according to the data, a parametric model with a mixture a gaussian functions for chrominance (global and local distributions), and a non-parametric method with loghistogram for the energy distributions. Due to the choice of the ICA filters and the assumption of independence between luminance and the two chromatic dimensions in the proposed color space, the dissimilarity between images can be naturally derived as a sum of simple Kullback-Leibler divergences, and then we justify the fusion process as a weighted sum of divergences :

) , ( . ) , ( g f D G F Diss i i KL i i ∑ = ω , (2)
with i=1,2,3 for color, spatial and energy distribution, respectively. For this first version of the system, all the ICA filters have the same weight. We use a symmetric version of the KL divergence, then this cumulative measure defines only a topological space. The weights will be adaptively tuned through the relevance feedback process. So for browsing, the system knows or can compute on line, for each couple of images, a triplet of dissimilarity values.

Starting with new browsing interfaces

Coming back to the idea of a user's workspace associated with the queries, we think that querying and managing a session should be integrated in a single environment, so that the user may find a flexible and intuitive process when using a image retrieval system. This can be done through a browsing strategy applied to organized image databases (images with neighborhood relationships). Because we must avoid the necessity for a user to find ONE query, the system should allow the user to build his sets of solutions without identifying it as the result of a single query. There exist interesting approaches allowing this way of interacting with a retrieval system, in the information retrieval domain [START_REF] Campbell | Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments[END_REF], in the user interface domain [START_REF] Nigay | A Framework for the Combination and Characterization of Output Modalities[END_REF], or in groupware technologies.

Our first approach with our system is to re-use these ideas in the context of a CBIR system.

Page by page browsing

This interface implements a "page by page browsing" base strategy. This can be useful to select a "zero page" to initialize a browsing. Each page identifies a cluster of images (automatically or manually extracted). The interface is based on the principle of a magnifying glass in order to view a great number of images. It also provides all tools allowing an easy browsing : zoom on interesting images, usage of a magnifier which size is also a parameter, …

Static browsing

Let us consider one unique matching function between documents, i.e. one unique dissimilarity function between images. By example, this function can be set with a choice by default for the weighting parameters in the cumulative dissimilarity. Figure 3 shows one of the interface we propose to query the system, inspired from the ostensive model [START_REF] Campbell | Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments[END_REF].

The idea is again that the user works in the context of a session to find the set of interesting documents he needs.

Instead of being a step by step interface, the system here shows its global interaction : from left to right the user queries using images and results. The results are displayed on a sphere, which allows a good visibility on the set of retrieved and selected documents. The global answer to the user is the set of images kept by the user, whatever the queries which were needed for that. We have also developed the necessary tools for the good usage of our interface : camera motion, zoom on the central documents, cut on not interesting documents, …

Dynamic browsing

This static model is interesting thanks to the global view of the session to express the information need. It can be enhanced by taking into account the sequence of the selected documents : this is the dynamic model.

If the sequence of the selected documents is used to adapt the matching function, that means the system answer depends not only on the selected documents but also on the own browsing sequence of each user. Browsing is a sequential process in time. In [START_REF] Campbell | Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments[END_REF], the authors point out that the user knowledge and his information need vary in time, and the system must take into account this evolution. Then in the ostensive model, temporal profiles are implemented to modulate the system answer. Three profiles are discussed, (i) constant in time, (ii) decreasing profile (forgetting factor), and (iii) increasing (memory factor). The second one is proposed in [START_REF] Campbell | Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments[END_REF] where the selected documents are more important in a near past than in a far past.

The integration of a temporal profile is one way to individualize the system answers between users, which may have different browsing strategies to reach the same target.

Even if this temporal aspect is important for a centered user approach, it remains limited due to the semantic gap, if it is not associated to a relevance feedback process. Now, we will explain how it is tightly coupled in this CBIR system.

Relevance feedback model 6.1 General principle

The relevance feedback model is one part of the interaction model we have defined, as it is illustrated at figure 3. At the top, we have a front end interface (here the "fish eye" viewer, cf §5.2). At the middle, the interaction model is composed of (i) a session model (ostensive model, cf §5) with a given temporal profile, (ii) a relevance model and (iii) a fusion model to integrate to different image feature representation (cumulative fusion, cf 4.3). And at the bottom, we have the result of the indexing process : dissimilarity between images for each feature space.

Relevance model

One of the major difficulty is to model the distribution of the relevant and non relevant documents, where multi modal distributions must be considered. In our system, we have used a non parametric method by kernel gaussian functions (K + , K - ). The user selects his relevant (R + set) and non relevant (R - set) documents and this relevant information is diffused to the neighborhood in each feature space. For an image G, in a feature space i, we have this Rocchio like equation :

)) , ( ( )) , ( ( ) ( s g Diss K s g Diss K G R i R S i i R S i i ∑ - ∑ = - + ∈ - ∈ + β α . ( 3
)
This relevance is integrated over time according to the given temporal profile. Then the dissimilarity proposed to the user U when he selects the image F and asks for its neighborhood, is defined by : {

} ) ( ); , ( . ) , ( G R g f Diss G F Diss i i i i i U Φ ∑ = ω . ( 4
)
This equation extends the equation ( 2) where the relevance of the image G is integrated to find the neighborhood of F. With the function Φ, the dissimilarity is linearly modulated in order to bring together images if the relevance is high. This measure is no more symmetric : the user relevance is only integrated for the images G which will be retrieved by the system from the selected image F. The relevance of the image F had been integrated in a previous query. The browsing framework is seen here as a chain along which the user relevance is diffused. At last, the weights ω i are estimated with simple heuristics which are often used in this framework, (i) inertia of the relevant documents, (ii) average rank and (iii) average relevance.

Experimentations

First experiments had been realized only on a limited database (321 scenes of indoors, landscapes (beach, mountain, forest, …), cityscapes). More extensive experiments on a larger database (several thousands) are works in progress. 16 subjects were invited to solve an information need in a given number of iterations (3, because the number of images these experiments is too small). The quality factor is estimated by the ratio of the number of relevant images at the last iteration versus all the relevant images in the whole session. Due to the limited size of the database, the temporal profile has not be integrated and has been held constant. In order to test one of the model dimensions, the other has been configured on the baseline. The first dimension concerns the fusion (the baseline is the inertia approach) and the second one, the relevance (the baseline is the model without the non relevance feedback) .

Conclusions, Perspectives

These first results confirm in this browsing framework, the role of the non-relevance judgment (α=2β) and shows an advantage for the fusion guided by the relevance ("sum" strategy) which is the more flexible approach, and is coherent with the choice of a non parametric method for relevance modeling. By this way, images are recoded in a new space which is a "relevance space" whose the dimension is the number of features. The underlying idea is to obtain a better clustering in agreement with the user relevance. This centered user approach is very promising, must be still developed to integrate more perceptual features and to improve this relevance model, to be validated by larger psychophysical experiments.

Figure 1 :

 1 Figure 1 : Example of a distribution according to a filter extracted by ICA. * means convolution.

Figure 2 :

 2 Figure 2 : Example of the "fish eye" view of a part of a session.

Figure 3 :

 3 Figure 3 : Three components of an interaction model inside a CBIR system.


Abstract. This paper describes two services of the Torii portal dedicated to the High Energy Physics research community, and developed within the context of the TIPS European project. These services both relate to the reuse of evaluations performed by humans on scientific publications. The first one, called QCT (Quality Control Tools) aims at collecting human detailed evaluations of documents in order to enrich the traditional topical indexing of documents with quality-related information. The second one, called SF (Social Filtering) integrates a push functionality as a alternate and complementary tool to traditional pull services such as Information Retrieval; documents are pushed to users with respect to the evaluations they have made in the past, and as compared to other users' evaluations.

Quality Control Tools and Social Filtering

This paper describes two services developed for a community portal, in the context of TIPS european project. After setting the context, we describe each of these services.

TIPS Project

1.1.1

Purpose of the Project TIPS (Tools for Innovative Publication in Science) is a european project funded by the European Community (IST-1999-10419) for the period February 2000 to July 2002. It involves the High Energy Physics research commmunity (HEP), that is in need of tools to access the great quantity of available information, especially the scientific articles that are published everyday in this domain.

The aim of the TIPS project is to develop tools that help researchers in their daily activities: search, and read articles, write and disseminate papers, communicate with other researchers.

1.1.2

Services in Torii Portal

The TIPS project has developed a Web portal in order to offer services to the HEP research community. The portal, called Torii, has two aims:

• centralize and facilitate the access to various third part resources, such as open archives of scientific publications, and • serve as a personal desktop where a given researcher can find the most relevant information for him. The Grenoble partner in TIPS project (CLIPS-IMAG research laboratory) is in charge of two services: Quality Control Tools, and Social Filtering. Both of these services are related to the evaluation of scientific publications.

Evaluation of Scientific Publications

The two services are related to the general idea of the evaluation of scientific publications. In their everyday activity, researchers need to evaluate articles that they read. Such evaluations occur in two different contexts: peer review, and evaluation for personal use.

1.2.1

Peer Review The peer review activity is a widespread process for evaluating the quality of research articles. For instance, when a journal calls for papers to be published in a new issue, the editorial board mandates researchers to review the submitted papers in order to decide whether an article reaches the quality standards required to be published in the journal. The reviewers are selected with respect to their personal research skills, that determine their ability to evaluate the article.

Reviewers are generally asked to fill an evaluation form that reflects the quality standards of the journal or conference. These quality standards can vary depending on the aim of the journal or conference.

1.2.2

Evaluation for Personal Use and Social Reuse Another context where evaluation occurs is the evaluation that a researcher does when he reads an article that he retrieved from some archive. When a researcher searches for information, he retrieves a number of articles selected on the basis of the topic that the articles deal with. Among the retrieved documents, some will be more relevant than others, because of quality-related features of the document.

To select the most relevant ones, the user needs to read the documents in order to elaborate his own opinion on the document. This opinion generally encompasses several criteria, that are likely to be shared by other users with the same information needs.

This evaluation process is generally not formalized, as the user does it for his own use. In some cases, it can be made partly explicit by the user when he tries to organize the set of interesting documents, not only by topic, but also by the extent to which it can be useful in some situation: for instance as a reference to quote in an article, or as a reference to recommend to students, or to research colleagues. QCT The Quality Control Tools (QCT) aim at collecting the evaluations that researchers have done, in order to enrich the traditional indexing information that is associated with documents for retrieval. Documents are generally indexed by information relating to topics. With human participation, quality-related information can be added, that is highly valuable to help subsequent users in their selection of the most relevant documents.

In Torii, a user can evaluate any document retrieved via the portal, by using an evaluation form. The portal keeps track of the evaluations made by any user, and combines them into statistical indicators that are displayed together with the document.

In order to easily produce ad-hoc evaluation forms, a tool to produce new forms has also been developed. This tool is dedicated to the editorial boards who would like to formalize the quality standards of their publications.

1.3.2

SF Social Filtering (SF), also known as Collaborative Filtering, automatizes the process of people recommending documents to other people that have similar interests. A typical social filtering system pushes documents to a given user on the basis of the evaluations that he has made in the past, as compared to the evaluations other people have also made.

In Torii, a user can evaluate any document that he encounters in the portal with a single click, and the system pushes relevant documents into a dedicated subfolder of his personal folder.

In the remainder of this paper, we describe the QCT and SF services that are available in Torii.

QCT in Torii

The Quality Control Tools collect user evaluations of documents and combines them into statistical indicators, that are in turn made available to all users.

Evaluation Criteria

A study has been conducted in order to identify important quality criteria for the evaluation of scientific publications.

Quality criteria are criteria that allow selecting a subset of documents out of a set of topically relevant documents. They allow users to specify quality standards when they search documents. Quality features are the properties of documents that are associated to quality criteria. For a given document, a quality feature must be assessed to allow for later searching along the corresponding quality criterion.

Fig. 1. Hierarchy of the full set of quality features.

There is a wide variety of quality features. For instance, "correctness" is a quality feature that reflects a strict meaning of the word "quality", as it vehicles a clear-cut judgment: it is always bad for a document not to be errorfree. On the opposite, "recency" is reflects a less clear-cut meaning for the word "quality", as a document may be a good one, although it is not a recent one.

Evaluation Forms

Evaluation forms reflect the quality standards that depend on the intended use of the evaluation. In Torii, the emphasis was made on the evaluations as made by a simple reader, for his own use. A standard default evaluation form was defined with the help of experts in the domain, for it to be easily understood and filled up by users. An additional functionality was also provided to account for the fact that users are not always willing to formalize their evaluations. This is the "free comment" functionality, that allows a user to comment freely on a document.

Besides this, the peer review context was also accounted for, with a tool that allows to create ad-hoc forms for more specific intended uses.

We present here the general structure of an evaluation form via the presentation of the user interface to create forms, the standard default evaluation form, and the "free comment" functionality.

2.2.1

Creating Forms In Torii, the definitions of evaluation forms are stored into a database, in order to automatically generate the corresponding forms.

We first describe the general structure of a form. -A form is a set of "scaled criteria".

-A scaled criterion is a criterion associated with a "scale" which can be a set of predefined values, or a free text field. -Each criterion can have a set of subcriteria, which are also associated with a scale.

-The predefined values of a scale can be associated with numbers when it is relevant to order these values.

Figure 2 shows a sample of user interfaces for creating forms. The administration tool allows to:

-Create new predefined values and associate numerical values to them, -Group predefined values to build a scale, -Create a new criterion, and optionally specify whether it is a subcriterion of another criterion, -Associate a criterion to a scale (to produce a scaled criterion), -Group criteria to build a form.

2.2.3

Standard Default Form Figure 3 shows the default evaluation form that is prompted when a user wants to evaluate a document in details. For the standard default form, to be used by simple readers as opposed to mandated reviewers, a selection of the most important quality features has been made, with the help of researchers in the HEP domain.

Three criteria remain: "Research quality", "Presentation" and "Intended audience". Research quality can be detailed along two subcriteria: "Validity" and "Importance".

Free Comments

Users can attach free comments to the documents they access via the portal. A given user can edit a private comment, only visible by himself, and a public comment, accessible to any other user.

Statistics

Statistics can be drawn from users evaluations of documents (Number of Evaluations, Research Quality, Presentation, Intended Audience). These statistics are then displayed when a user accesses a document via the portal.

SF in Torii

The Social Filtering service in Torii pushes documents into the personal folder of an authenticated user.

General Principle of Social Filtering

The profile of a given user is the set of document identifiers associated with the evaluation that the user has given in the past for these documents.

For a given user U, the filtering engine computes a score for each document that is new to U, but has already been evaluated by one or several other users. This score is the prediction that the system makes of how much user U will like the document. It accounts for the evaluation of the other users, weighted by the similarity between the profile of these users and the profile of user U. The engine only pushes the documents for which the prediction is over a given threshold.

In Torii, the engine follows the Pearson-R correlation method for the computation of profile similarity, and the standard memory-based algorithm for the computation of the ranking (see for instance [START_REF] Resnick | GroupLens: An Open Architecture for Collaborative Filtering of Netnews[END_REF]).

Torii Interfaces for Social Filtering

The Social Filtering service allows users to indirectly define their profile, via a rating of documents on a single scale with the meaning "I like it". Users can check the documents that have been pushed by the system into a specific subfolder of their personal folder.

Conclusion and perspectives

The two services presented in this paper increase the possibility of reusing individual evaluation efforts for the benefit of a community. They tackle the information overload problem that researchers encounter, by encouraging and amplifying some of the naturally arising social phenomena.

For the time being, Torii integrates several services dedicated to the personalized and assisted access to information. QCT and SF are two of them, but cognitive filtering and assisted information retrieval are also available. Cognitive filtering, based on the topical content of documents, also requires evaluation feedback from the users, as well as information retrieval (the well-known "relevance feedback"). A stronger integration of these related services will be studied for the future, in order to allow users to better benefit from the feedback that they provide in these various contexts.

  385, rue de la Bibliothèque -B.P. 53 38041 Grenoble Cedex 9 -France Nathalie.Denos@imag.fr

1. 3

 3 Overview of QCT and SF Services 1.3.1

Fig. 2 .

 2 Fig. 2. Sample of interfaces for the creation of a form.

Fig. 3 .

 3 Fig. 3. Default evaluation form for the simple reader. The forms interface allow users to expand and collapse the scale for each criterion. The right-hand column recalls to the user whether he has already evaluated a criterion or not.

Fig. 4 .

 4 Fig. 4. Free comments functionality. A user can both edit his own comments and view other users comments in this window.

Fig. 5 .

 5 Fig. 5. Display of the QCT statistics in the document view.

Fig. 6 .

 6 Fig. 6. User interface for the rating of a document to be used for social filtering. The number of red stars indicate how much user U likes the document, and the grey stars indicate the system prediction of this value, when available.

Fig. 7 .

 7 Fig. 7. The "Recommended" subfolder, that contains the documents pushed by the system.

We have identified the following 9 top-level quality features (for more details, see document UR-R1-QCT1 from the TIPS project Web site http:// tips.sissa.it). The full set of candidate quality features is given in figure 1.


Jean-Pierre Chevallet
email: jean-pierre.chevallet@imag.fr

Jian-Yun Nie
email: nie@iro.umontreal.ca

Intégration des Analyses du Français dans la Recherche d'Informations

Cet article décrit les approches que nous avons implantées dans le cadre d'une collaboration de recherche entre nos deux groupes. Ces approches visent à créer une représentation plus précise pour les documents et les requêtes dans un système de recherche d'informations (RI). Elles sont basées sur des extractions de termes composés, au lieu de termes simples utilisés dans les approches traditionnelles. Deux approches d'extraction sont employées: par une analyse syntaxico-statistique, et par l'utilisation d'une base de terminologie manuelle. Nous décrivons ces deux approches, ainsi que les résultats préliminaires obtenus.

-

Objectif

La plupart des systèmes de recherche d'informations (RI) utilisent des mots simples pour représenter des documents et des requêtes. Il est reconnu depuis longtemps que cette représentation est insuffisante pour permettre une recherche orientée vers la précision. Par exemple, un besoin sur "recherche d'informations" est exprimé par une requête "recherche et information". Cette requête trouvera, non seulement les documents sur "recherche d'informations", mais également ceux sur "information pour la recherche", voire les documents qui ont employé les mots "recherche" et "information" mais dans deux contextes totalement indépendants. Un taux important de bruit est donc produit.

Pour ce problème d'imprécision dans la représentation, il est souvent suggéré que des termes composés, au lieu de mots simples, seraient la solution. Deux approches différentes ont été proposées pour extraire des termes composés de documents: la première à l'aide d'une base de terminologie établie manuellement, et la seconde par une analyse syntaxicostatistique sur des groupe de mots et sur leur co-occurrences. Ces deux approches ont été utilisées séparément. Aucune comparaison entre elles n'a été effectuée. L'objectif de notre recherche conjointe dans le cadre du Programme de la coopération France-Québec est, d'une part, de renforcer les approches existantes par des analyses linguistiques et statistiques plus poussées et de les appliquer aux textes en français, et d'autre part, d'effectuer une comparaison entre les deux approches.

Cet article décrit les approches proposées par les deux groupes de recherche en collaboration. Il est à noter que cette recherche est encore en cours. Le premier objectif a été atteint: les deux groupes ont implanté leur approche respective. Mais la comparaison entre les deux approches n'a pas encore été effectuée.

Etat de l'art de l'extraction de terme composés

Dans cette section, nous décrivons brièvement les approches existantes pour extraire des termes composés.

Construction d'un thésaurus

Les premiers systèmes de recherche d'information, ont utilisé un vocabulaire contrôlé pour l'indexation des documents. Une liste de termes simples ou composés, est établie manuellement par des experts en documentation et des experts d'un domaine spécialialisé. Il s'agit en fait d'un thésaurus de termes avec un seul type de relation : la relation spécifique/générique. Par exemple, la classification ACM [START_REF]The full ACM computing classification system (1991 version)[END_REF] contient quelques 1200 termes organisés en 4 niveaux. Nous en donnons ci-après un exemple traduit en français: Les auteurs doivent eux-mêmes indexer leurs documents en utilisant les termes du thésaurus. Dans le système MEDLINE [START_REF] Mccarn | MEDLINE: An introduction to on-line searching[END_REF] un thésaurus similaire est utilisé. Plus récemment [START_REF] Lee | Information retrieval based on conceptual distance in IS-A hierarchies[END_REF] propose une nouvelle méthode d'ordonnancement des documents qui tient compte des données d'un thésaurus. Malheureusement, ces techniques sont limitées par la trop faible taille des thésaurus utilisés. Quand la taille du thésaurus augmente, d'autres problèmes peuvent subvenir. Par exemple, dans un thésaurus général (e.g. Wordnet) où beaucoup de domaines sont confondus, un terme peut avoir des sens différents dans différents domaines. Si le sens du terme dans le domaine de l'application n'est pas identifié, une utilisation ultérieure de termes reliés à ce sens conduira à retrouver beaucoup de documents nonpertinents et propoquera du bruit. Si un thésaurus, ou plus généralement une base de terminologie, est utilisé pour la RI, il est nécessaire de prévoir un mécanisme de désambiguïsation ou de sélection selon le domaine de l'application.

D
Parallèlement, des études ont été menées sur la construction automatique de thésaurus et sur l'identification automatique de termes composés [START_REF] Bruandet | Construction automatique d'une base de connaissances du domaine dans un système de recherche d'informations[END_REF][START_REF] Bruandet | Construction de thésaurus dans le système de recherche d'information IOTA: application à l'extraction de terminologie[END_REF]. L'avantage d'un tel thésaurus construit automatiquement est qu'il ne nécessite pas d'entretien manuel, et qu'il suit automatiquement l'évolution du corpus (et du domaine). La construction automatique du thésaurus est basée sur le principe suivant : si deux termes co-occurrent fréquemment dans le même texte, alors ils ont une chance de former un concept particulier. Cette technique doit être accompagnée d'une analyse grammaticale des termes pour éliminer les mots outils qui forment des expressions inutiles à l'indexation ("c'est-à-dire", "bien sûr", etc). On trouve dans [START_REF] Fagin | Experiments in Automatic Phrase Indexing for Document Retrieval: A Comparison of Syntactic and Non-Syntactic methods[END_REF], une approche similaire basée sur la recherche de motifs syntaxique. Ces travaux montrent que paradoxalement, les performances du système se dégradent avec l'utilisation des termes composés. Plusieurs autres études [START_REF] Lewis | Term clustering of syntactic phrases[END_REF][START_REF] Smeaton | Progress in the application of natural language processing to information retrieval tasks[END_REF] ont essayées d'améliorer les résultats de Fagin et montrent que le problème se trouve dans la désambiguïsation des expressions complexes. Par exemple dans l'expression "évolution du processus d'apprentissage de la lecture" il est difficile d'automatiser le découpage correct des relations entre les termes. De manière simple, le système devra considéré toutes les expressions: "évolution du processus", "évolution d'apprentissage", "évolution de la lecture", etc. Cela entraîne beaucoup de bruit et explique en partie les mauvais résultats que l'on peut obtenir.

Utilisation de relations entre les termes

Une autre utilisation de thésaurus dans la RI réside dans le processus d'extension de requête ou dans des approches associatives. Cela consiste à ajouter des termes reliés dans une requête initiale afin de l'enrichir. Une requête étendue peut retrouver plus de documents reliés, ainsi augmenter le taux de rappel. Dans [START_REF] Salton | On the use of spreading activation methods in automatic information retrieval[END_REF], il est précisé que la difficulté réside dans l'identification de l'ensemble des termes proches des termes de la requête originale. En fait, le nombre des termes possiblement reliés aux termes d'une requête est très important et risque de faire dériver la requête vers un thème non désiré par l'utilisateur.

Les bases terminologique pour la RI étant très rares, les solutions couramment adoptées sont celle d'un calcul de distance entre les termes, basé sur la fréquence de leur cooccurrence. Une autre méthode pour découvrir des relations entre les termes [START_REF] Güntzer | Automatic thesaurus construction by machine learning from retrieval sessions[END_REF][START_REF] Kimoto | Construction of a dynamic thesaurus and its use for associated information retrieval[END_REF], consiste à stocker les choix de l'utilisateur au moment du bouclage de pertinence (relevance feedback). Le principe suivant est appliqué : si deux documents sont jugés pertinents pour une même requête, cela signifie qu'il existe une relation entre eux, donc il doit aussi exister une relation entre l'ensemble des termes qui les indexent. Au fur et à mesure que les bouclages de pertinence s'accumulent, il est possible de dégager certaines relations entre des termes ou entre de petits groupes de termes.

En fait, ces méthodes permettent d'aider l'utilisateur, mais leur capacité à réellement augmenter l'efficacité d'un système, reste encore à prouver. Les résultats sont en fait très variables selon les systèmes et les corpus utilisés [START_REF] Sparck-Jones | Notes and references on early automatic classification work[END_REF].

L'approche basée sur une analyse syntaxico-statistique

Nous avons expérimenté à l'aide du système IOTA [START_REF] Chiaramella | IOTA: a full test information retrieval system[END_REF] une approche d'indexation automatique basée sur une analyse syntaxique et grammaticale de surface. La terminologie n'est pas établie manuellement, mais constituée automatiquement à partir du corpus. Cette approche est conçue dans le but de repérer des termes dans les domaines pour lesquels aucune terminologie manuelle est disponible, ou pour enrichir une terminologie manuelle. La condition préalable pour appliquer cette approche est d'avoir un corpus de grande taille, concentré sur un ou quelques domaines. Autrement, s'il y a une trop grande dispersion de domaines couverts par un corpus en rapport avec une trop faible taille, la qualité des termes extraits par cette approche diminuera. L'enchaînement des traitements est illustré dans la figure 1. On peut y voir deux modules détachés. Le premier concerne tous les traitements concernant l'analyse grammaticale. Le résultat de cette analyse est utilisé dans le second module qui construit automatiquement un thésaurus selon les co-occurrences de mots tout en considérant les caractéristiques syntaxiques.

L'analyse grammaticale

En entrée de ce module nous avons le texte brut dans un format reconnu par le système (actuellement TEI et HTML). Après conversion et analyse lexicale, le texte passe au travers d'un analyseur de surface qui utilise un dictionnaire associé à un modèle morphologique. Il est à noter que le dictionnaire utilisé n'est pas complet, c'est-à-dire qu'un certain nombre de mots sont inconnus. Il sera donc nécessaire d'avoir un traitement pour les mots inconnus.

La phase de lemmatisation a pour objectif la normalisation des termes. Par exemple, les mots "recherche" et "rechercher" seront lemmatisés en une même forme. Cette étape permet de comparer les mots sémantiquement proche, ainsi augmenter le rappel de la recherche. La phase d'étiquetage vise à retrouver les groupes nominaux. Cette phase [START_REF] Palmer | Etude d'un analyseur de surface de la langue naturelle: application à l'indexation automatique de textes[END_REF] est basée sur une catégorisation de mots et une sélections des solutions gramaticales. La catégorisation assigne toutes les catégories grammaticales possibles aux mots, en utilisant le dictionnaire. Cependant, cette catégorisation est très ambiguë: un mot peut être assigné à plusieurs catégories. Par exemple, le mot "porte" est à la fois un nom et un verbe. On remarque dans le résultat de cette étape que beaucoup de catégories assignées sont impossible en français. Par exemple, un article ne peut pas être suivi par un verbe. Ainsi, nous procédons une élimination de catégories impossible en considérant les successions possibles des catégories en français. Dans ce même traitement, les mots inconnus peuvent aussi être assignés à une catégorie selon leur morphologie. Un deuxième filtrage est ensuite appliqué pour réduire davantage le nombre d'ambiguïtés. Ce filtrage consiste à utiliser des schémas de résolution répertoriés manuellement. Chaque schéma de résolution correspond à un cas d'ambiguïté typique et dont la résolution est connue.

La sortie de ce module est une collection de textes étiquetés, c'est-à-dire dont tous les mots ont été catégorisés et lemmatisés. Le deuxième module part de cette collection étiquetée et construit un thésaurus pour le corpus.

L'extraction de terminologie

L'extraction de la terminologie se fait sur des bases statistiques [START_REF] Bruandet | Modèle partiel de connaissances pour un système de recherche d'informations[END_REF][START_REF] Bruandet | Outline of a knowledge base model for an intelligent information retrieval system[END_REF][START_REF] Bruandet | Construction automatique d'une base de connaissances du domaine dans un système de recherche d'informations[END_REF]. En effet, nous prenons pour hypothèse que la fréquence de cooccurrence de termes dans un corpus de très grande taille, reflète la terminologie employée. Cette mesure de cooccurrence doit tout de même tenir compte de la catégorie grammaticale des termes. Ainsi, selon leur catégories, l'apparition de deux mots peut être considérée comme une co-occurrence ou non. En utilisant une mesure statistique, on élimine les groupes de faibles fréquences. Le résultat de cette analyse est un graphe qui exprime les connexions entre les mots. Dans ce graphe, nous traitons en particulier des connexions en formes de clique (des sous-graphes dans lesquelles tous les noeuds sont inter-reliés). D'une part, l'ensemble ou un sous-ensemble des mots appartenant à un même clique sont considérés comme un terme composé ou multi terme possible. D'autre part, ces mots sont considérés comme étant fortement reliés. Nous avons donc aussi une relation créée entre les mots ou les termes à l'intérieur de ce graphe. Ainsi, le graphe peut être considéré comme une sorte de thésaurus. A l'expérimentation, la plupart de ces cliques correspondent à des multi termes porteurs d'un sens important pour le corpus.

L'utilisation du thésaurus dans l'indexation

L'indexation consiste à sélectionner des groupes nominaux significatifs du texte analysé. Un groupe est significatif pour la recherche d'information s'il est répertorié dans le thésaurus précédemment construit. La sélection des termes fait aussi intervenir le processus de cassure de l'expression [START_REF] Kerkouba | Une méthode d'indexation automatique des documents fondée sur l'exploitation de leurs propriétés structurelles: Application à un corpus technique[END_REF]. Cela consiste à casser une longue expression en des termes plus petits inclus dans le thésaurus (une clique). Par exemple, dans le groupe nominal extrait d'un texte de notre corpus de test "pollution de l'air par des gaz d'échappement des moteurs diesel", nous pouvons sélectionner les index suivant après cassure "pollution de l'air", "moteur diesel". Les mots "gaz" et "échappement" n'ont été retenu qu'au titre de termes d'indexation individuels car il ne présentait pas, au sein de la collection, une force de cohésion suffisante pour être retenus.

Une pondération p du type tf*idf est ensuite associée à chaque termes (mono et multi) selon la formule suivante : p = fd * log (N / df) avec fd l'importance du terme dans le document, déterminée par le rapport entre le nombre d'apparition et la taille du document; N le nombre total de documents dans la collection et df le nombre de documents où le terme apparaît. L'analyse d'une requête suit le même processus de traitement que les documents. L'analyse morphologique est cependant un peu plus délicate à réaliser dans le mesure où la taille de la requête est souvent assez faible et que surtout, les requêtes se présentent rarement sous la forme de phrases bien formées. Notre analyseur morphologique de surface a alors des difficultés à analyser et désambiguïser efficacement ces requêtes. Il a donc été nécessaire d'adapter les traitements précédents. Les traitements sur une requête sont illustrés dans figure 2.

Après lemmatisation et cassure des groupes conceptuels à l'identique de l'indexation, une étape de reformulation est effectuée dans le but de construire une requête mélangeant les mono et multi termes. Plusieurs formulation ont été étudiées: les mono termes seul, les groupes conceptuels (ou multi termes) seul, et une combinaison des deux types de termes.

Les résultats préliminaires seront décrits dans la section suivante. Nous avons également expérimenté plusieurs fonctions de comparaisons. Il s'est avéré que les fonctions prenant en compte la taille de la requête dans la comparaison sont nettement meilleures que les autres fonctions. Il faut noter aussi, que la différence très nette de valeur de pondérations entre les mono et multi termes, nous a conduit à systématiquement ajouter aux poids des multi termes un facteur multiplicatif constant. Cette valeur, choisie de manière empirique égale à 5, a sensiblement compensé la faible apparition des groupes conceptuels dans les textes. Nous n'avons pas encore intégré le système de reformulation automatique en fonction du profil de l'utilisateur [START_REF] Chiaramella | IOTA: a full test information retrieval system[END_REF].

Analyse des résultats

Nous avons expérimenté notre système principalement sur une collection (collection Od1) de l'OFIL dans le projet Amaryllis de l'AUPELF-UREF. La courbe de la figure 3 montre un exemple de répartition des termes selon leur nombre d'occurrence et leurs poids. On note de manière claire que les mono termes sont en large majorité par rapport aux groupes conceptuels (multi termes). Cette situation est en fait volontaire car nous avons choisi un très fort filtrage des groupes conceptuels afin d'obtenir le maximum de groupes conceptuels significatifs. La seconde courbe (fig. 4) montre le rapport entre le rappel et la précision sur toutes les requêtes résolues de la collection od1 de Amaryllis. Sur ces courbes apparaissent les divers essais de combinaison des groupes conceptuels avec les mono termes. En ce qui concerne les groupes conceptuels (multi termes) seuls, gain qualitatif global n'apparit pas de manière claire. Les courbes incluant les mots clés seuls sont presque toujours au-dessus des autres courbes. Cela est sans doute dû à la trop forte sélection opérée au moment du filtrage sur les groupes conceptuels. Il faut noter cependant qu'au niveau du rappel de valeur 1, cette courbe se situe au dessus des autres, cela signifie que l'on atteint le rappel maximum avec moins de documents. Cela tend à montrer que les groupes conceptuels favorisent la précision des réponses. On peut également percevoir ce résultat en observant la courbe (non donnée ici) de la précision par rapport au nombre de documents retrouvés. On s'aperçoit que systématiquement, la précision est maximum entre 20 et 30 documents retrouvés. De plus cette valeur de précision est supérieure aux autres combinaisons. Ce nombre de document est encore élevé, mais il est encore raisonnable pour un nombre de documents du corpus très important. Il est en effet possible de parcourir une telle liste de documents si l'on est sûr d'y trouver des documents pertinents.

L'approche basée sur une base de terminologie manuelle

Dans cette approche, l'extraction de termes composés des documents, ainsi que l'expansion de la requête par des termes reliés se font à l'aide d'une base de terminologie manuelle. Cette base de terminologie doit contenir un grand nombre de termes reconnus dans le domaine d'application, d'une part, et un grand nombre de relations entre ces termes, d'autre part. Dans notre cas, nous avons utilisé la Banque de Terminologie du Québec (BTQ). Cette banque renferme beaucoup de termes, mais malheureusement pas beaucoup de relations entre les termes.

L'expérimentation a été menée sur un large corpus de documents : la collection de l'Institut d'Informatique Scientifique et Technique (INIST). Cette collection contient 163 308 documents (résumés) en français dans tous les domaines scientifiques. Cette expérimentation a été décrite dans [START_REF] Nie | Using terminological bases in information retrieval[END_REF] apparu dans ces mêmes actes. Nous rappelons simplement que cette approche consiste en les deux étapes suivantes:

-extraction des termes reconnus par la BTQ des documents du corpus; -extension de la requête par les termes reliés. Dans notre expérimentation effectué sur le corpus INIST, la première étape a donné de bons résultats: la plupart des termes décrits dans les documents sont reconnus par la BTQ. La représentation des documents par les termes de la BTQ est donc une représentation assez complète et précise. Nous avons utilisé un schéma de pondération (tf*idf) qui a été conçu pour les mots simples. Il n'est donc pas adapté à notre représentation par les termes de la BTQ. Cela est dû au fait qu'à partir d'un groupe de mots qui correspond à un terme composé, on peut souvent extraire des termes simples, également reconnus par la BTQ. Par exemple, à partir de "détonateur électrique à retard", les termes suivants peuvent être extraits: "détonateur électrique à retard", "détonateur électrique", "détonateur", "électrique". Ce groupe de mots est donc sur-représenté dans le résultat de l'indexation par la pondération utilisée. A cause de ce problème, d'une part, et du manque de relations entre les termes dans la BTQ, d'autre part, l'expérimentation que nous avons conduite en utilisant la BTQ dans le modèle booléen, sur le corpus INIST, n'a pas donné d'améliorations. Au contraire, la performance obtenue est moins bonne que le modèle classique utilisant des mots clés. Toutefois, nous pensons que les causes principales sont les deux problèmes que nous venons de mentionner et nous ne remettons donc pas en cause l'apporte positif potentiel de la BTQ à la RI mais nous tentons d'améliorer notre approche.

Après l'étude décrite dans [START_REF] Nie | Using terminological bases in information retrieval[END_REF], nous avons reconduit l'expérimentation en utilisant le modèle vectoriel. Rappelons que dans modèle vectoriel, les documents et les requêtes sont représentés par des vecteurs dans un espace à n dimensions. Ces dimensions correspondent à tous les termes élémentaires utilisés comme index :

< t 1 , t 2 , ... , t n > d × <w d1 , w d2 , ..., w dn > q ×
<w q1 , w q2 , ..., w qn > où w di et w qi représentent respectivement les poids des termes t i dans le vecteur document d et dans le vecteur requête q.

Le calcul de la correspondance est établi à l'aide d'une mesure qui calcule le cosinus de l'angle formé par ces deux vecteurs.

Σ i (w di * w qi ) sim(d, q) = ------------ [Σ i (w di 2 ) * Σ i (w qi 2 )] 1/2
Il y a deux méthodes de base pour réaliser l'extension de requêtes dans le modèle vectoriel :

1) par ajout de termes dans le même vecteur;

2) par création d'un vecteur additionnel pour la requête.

Dans le premier cas, cela correspond à faire passer le poids d'un terme du vecteur requête de nul à non nul. Les documents contenant ce terme verront alors leur mesure de similarité augmenter. Dans le second cas, tous les termes ajoutés forment un nouveau vecteur. Le calcul de la correspondance doit alors s'effectuer en deux temps : d'abord le calcul de la similarité avec le vecteur initial, puis le calcul avec le vecteur d'extension. Le résultat final est alors une combinaison pondérée des deux valeurs de similarité.

Dans nos expérimentations, ces deux méthodes d'extension ont été testées. Cependant, dans tous les cas, la première méthode (celle qui mélange les termes ajoutés avec les termes initiaux) donne de meilleurs résultats. Ici, nous décrivons seulement les résultats obtenus par la première méthode.

Dans la table 1, nous résumons les trois tests effectués:

1) seuls les mots isolés ont été utilisés pour l'indexation, et la BTQ n'est pas utilisée (méthode classique).

2) seuls les termes de la BTQ ont été utilisés pour les documents et les requêtes; l'extension de la requête a été effectuée à l'aide des relations de la BTQ .

3) à la fois les mots isolés et les termes composés ont été utilisés pour les documents et les requêtes; l'extension de la requête a été effectuée à l'aide des relations de la BTQ.

Remarques et travaux futurs

En recherche d'information, l'usage des thésaurus et des bases terminologiques (créés manuellement ou automatiquement) est classiquement reconnu comme une source d'amélioration potentielle au fonctionnement du système. Beaucoup de travaux ont été faits sur ce sujet, malheureusement, les résultats ont souvent été jugés décevants. Cependant, rien ne nous permet de mettre en cause l'utilité potentielle de telles ressources parce que nos approches restent encore très simples, et des améliorations sont à apporter.

Les résultats obtenu dans la première approche sont encourageant sur certains aspects, mais néanmoins restent en deçà de ce que nous attentions. L'impact sur la qualité des réponses du système en utilisant les multi termes n'a pas été facile à mettre en évidence. En effet, la formulation automatique des requêtes en une composition de mono termes et multi termes n'a pas produit d'amélioration significative sur la collection de test Amaryllis. Par contre, lorsque nous avons choisi manuellement des multi termes parmi les termes proposés par le système, nous avons constaté une forte amélioration dans la précision des réponses. Cela tend à prouver qu'il est nécessaire de disposer d'un thesaurus comprenant des liens entre les termes. Actuellement, ni la Base Terminologique du Québec, ni la construction automatique de la terminologie, nous fournit ce genre d'information de façon suffisante et précise. Nous pensons qu'il est possible d'aller dans le sens d'un calcul automatique des liens entre nos termes. Dans [START_REF] Grefenstette | Exploration in automatic thesaurus discovery[END_REF] on trouve une approche similaire à la nôtre mais basée sur des termes simples. Cette approche de construction automatique sans aucune connaissance préalable donne déjà des résultats encourageants. L'auteur de plus indique qu'une partie du bruit produit lors de cette construction provient de l'ambiguïté des termes simple. Il préconise l'usage de multi termes, c'est l'approche que nous avons choisi ici. Nous pensons donc poursuivre nos expérimentations sur la recherche automatique de liens entre multi termes en se basant sur le même principe que [START_REF] Grefenstette | Exploration in automatic thesaurus discovery[END_REF] qui consiste à considérer que deux termes ont un rapport sémantique d'autant plus probable que leur contexte d'utilisation est semblable.

Dans la deuxième approche, bien que la base terminologique utilisée soit de très grande taille, l'amélioration des performances du système de RI n'est pas vérifiée. Cela semble dû au faible nombre de relations sémantiques présentes dans cette base, d'une part, et le schéma de pondération non adéquate, d'autre part. Dans nos travaux futurs, nous allons traiter ces deux problèmes, et nous essayerons de combiner les deux approches décrites ici. En effet, les deux approches décrites ont leur qualités complémentaires. 1) D'un côté, une base de terminologie manuelle possède une qualité contrôlée. Cependant, cette base est statique. Pour de très grande corpus, il est difficile de garantir que cette base puisse couvrir tous les termes utilisés. De plus, les termes utilisés évoluent très vite dans certains domaines (ex. informatique). Une approche uniquement basée sur une base de terminologie manuelle sera très vite insuffisante, quelque soit la taille de la base. 2) D'un autre côté, l'approche basée sur le calcul de la terminologie à partir du corpus ne requiert pas d'intervention manuelle, et la terminologie produite est un reflet du corpus traité. Ainsi, il peut suivre l'évolution du domaine. Cependant, on ne peut souvent pas garantir la qualité des termes extraits. Dans nos travaux futurs, nous envisageons la combinaison des deux approches dans l'extraction des termes, comme illustré dans la figure 6: Les termes de la BTQ seront d'abord repérés. Pour la partie non repérée, l'approche syntaxico-statistique sera appliquée afin de repérer des termes inconnus de la BTQ. Cette combinaison rallie la précision de la base de terminologie manuelle avec la flexibilité de l'approche syntaxico-statistique.

Fig. 1 :

 1 Fig. 1 : schéma global de l'indexation

Fig. 2 :

 2 Fig. 2 : Traitements sur des requêtes

Fig. 3 :

 3 Fig. 3 : répartition des termes d'indexation

  Fig. 4 : interrogation

Fig. 5 .

 5 Fig. 5. Extraction de termes à l'aide de la BTQ

Fig. 6 .

 6 Fig. 6. Combinaison des approches

Table 1 .

 1 Expérimentation avec une base de terminologie manuelleComme nous pouvons le voir dans la table 1, l'usage unique des termes de la BTQ (méthode 2) dégrade les performances du système. Cependant, quand on combine les mots avec les termes dans l'indexation, on observe une légère amélioration. Cette amélioration, bien que faible, nous montre que la BTQ est potentiellement utile à la RI, car la combinaison testée est encore très simple. Nous pensons qu'il est possible, avec une combinaison plus sophistiquée d'améliorer de manière plus significative les résultats.

	Méthode	1	2	3
	Rappel		Précision
	0.0	0.7335	0.6164	0.7322
	0.1	0.4101	0.4304	0.4181
	0.2	0.2459	0.2249	0.2983
	0.3	0.1593	0.1540	0.1605
	0.4	0.1222	0.1056	0.1251
	0.5	0.1017	0.0821	0.1053
	0.6	0.0776	0.0562	0.0785
	0.7	0.0487	0.0346	0.0508
	0.8	0.0278	0.0151	0.0268
	0.9	0.0101	0.0045	0.0105
	1.0	0.0024	0.0004	0.0030
	Précision moyenne	0.1763	0.1567 (-11.12%)	0.1826 (+3.57%)