C Godet

S Ababou-Girard

B Fabre

Y Molard

A B Fadjie-Djomkam

S Députier

M Guilloux-Viry

S Cordier

Surface immobilization of Mo 6 I 8 octahedral cluster cores on functionalized amorphous carbon using a pyridine complexation strategy

Keywords: tetrahedral amorphous carbon, pulsed laser deposition (PLD), bonding configurations, surface characterization, electrical properties

from the binding energy of Mo core levels, is also compared to that of the precursor (polycrystalline powder). The number of surface anchoring sites is also deduced from XPS analysis to estimate the efficiency of the immobilization process. Electrical transport properties of metal-insulator-semiconductor (MIS) diodes obtained from functionalized a-C films were also investigated using a mercury drop as the soft electrical top contact to check possible changes in the electronic properties of a-C after the alkyl monolayer grafting and whether some signature of the monolayer of electroactive [Mo 6 I 8 ] 4+ clusters can be detected after immobilization.

Experimental

The ). Before deposition, the target was pre-cleaned for 5 minutes at 2 Hz with a shutter in front of the substrate. After deposition, nitrogen gas was introduced in the chamber and the carbon films were quickly transferred to the XPS setup or to the grafting setup under a dry nitrogen atmosphere. The cluster chemistry includes three major preparation steps, as summarized in Scheme 2.

Preparation of the

Step I: the reaction of

XPS analysis.

After exposure to ambient atmosphere for a few minutes, the grafted carbon surfaces were introduced in the UHV chamber and kept at 1×10 -9 mbar for several hours before XPS analysis.

XPS measurements were performed with Mg K (h = 1254 eV) X-ray source, at an emission angle , using a VSW HA100 photoelectron spectrometer with a hemispherical photoelectron analyzer, at 22 eV pass energy, providing an experimental resolution of 1.0 eV. Spectral analysis included a Shirley background subtraction and peak separation using mixed Gaussian-Lorentzian functions. Peak area ratios quoted in this work have been corrected for the photoemission cross section of each chemical element. Additional measurements were performed using a monochromatized Al K (h = 1486.6 eV) X-ray source to obtain a better peak separation for specific core levels.

In addition to the XPS analysis of metal (Mo

Electrical Transport Measurements.

Electrical transport measurements were performed on Hg // Mo 6 I 8 / pyridinealkyl / a-C / p-Si junctions, using a mercury drop as a top electrode because its large surface tension (480 mJ m - -voltage (V) characteristics were obtained at room temperature using a Keithley 6487 picoammeter and a home-made setup with hanging Hg drop and a micromanipulator to adjust the contact area at ~ 0.3 mm2 . A fresh Hg drop (99.999%, Fluka) was used for each measurement, at several locations on the sample surface. An ohmic contact was made on the scratched rear side of the p-type Si substrate (resistivity 1-5 .cm) by applying a silver paste electrode. In this work, the direct current transport characteristics were measured in a glove box with a humidity level of 22-25 % of the water saturation pressure, after XPS measurements (under UHV) and a brief exposure to the ambient.

Results

Amorphous carbon

The a-C film thickness, d a-C = 30 nm, and optical properties were determined from spectroscopic ellipsometry. The refractive index n (2 eV) = 2.63 is characteristic of a high density material [43], as confirmed by previous X-ray reflectivity measurements (= 2.84 g.cm -3 ) [START_REF] Zebda | Surface energy and photoelectron spectroscopy studies of amorphous carbon surfaces[END_REF] and bulk plasmon energy (E P = 29.5±0.2 eV) given by photoelectron energy loss spectroscopy; the surface hybridization of the as-grown a-C film (sp 3 / sp 2 + sp 3 ) was found to be 0.5-0.6 from XPS measurements using a monochromatic X-ray source [37, 44].

Under optimized PLD conditions including the pre-cleaning of the target, amorphous carbon films suitable for monolayer grafting experiments are obtained, with a very smooth surface (rootmean-square roughness ~ 0.3 nm) as shown by Atomic Force Microscopy (AFM).

Pyridine-functionalized a-C

A C C E P T E D M A N U S C R I P T

The XPS survey spectrum of the pyridine-modified carbon surface reveals characteristic peaks from the carbon substrate itself and from the C 1s, O 1s and N 1s core levels of the attached organic molecule (Fig. 1a). The shape and the intensity of these spectra as well as their relative positions in energy are consistent with the monolayer composition, with some additional O 1s contribution due to some adventitious adsorbed oxygen or water molecules.

The N 1s core level is characterized by a main peak at 400. The ratio between the total Mo 3d 5/2 area and the main I i 4d peak area is estimated at 0.77±0.05 which is close to the expected value for a [Mo 6 I 8 ] 4+ cluster core (0.75). However, the ratio between the Mo II (main peak) area and the I i 4d peak area is estimated at 0.66±0.05, indicating that the Mo * component must be included in the cluster composition. The cluster coverage,  = 9.210 12 cm -2 , deduced from the Mo 3d intensity using Equation 1, is consistent with a monolayer of bound [Mo 6 I 8 ] 4+ cluster cores.

Besides the Mo 3d, the Br 3d core level clearly shows two components with different binding energies, in contrast with the iodine core atom and the counter anion elements (S and F). Let us consider first the bromine labelling element core levels. Both the weaker (Br 3d 3/2 , Br 3d 5/2 ) doublet near 70 eV (Fig. 5) and the stronger (Br 3p 1/2 , Br 3p 3/2 ) doublet near 183 eV (Fig. . The peak area ratio (S 2p / I 4d) is 0.21, which means that, on average, each bound metallic cluster is surrounded by 1.8 triflate units. This ratio is obtained with a good precision; however, this value might be underestimated because some triflate moieties may be lost during XPS measurements, as reported previously [START_REF] Fabre | Electrochemical and charge transport behavior of molybdenum-based metallic cluster layers immobilized on modified n-and p-Type Si(111) surfaces[END_REF].

In summary, the Br 3d intensity provides (x -y) = 1.5±0.3 bromopyridine unit per bound metallic cluster and I 4d decomposition gives (6 -x) = 1±0.1 apical iodine per cluster, on average.

It can be concluded that each metallic cluster is bound to the organic monolayer surface through either three or four pyridine anchoring groups, corresponding respectively to (CF 3 SO 3 ) 3 [Mo 6 I i 8 I a 1 (Py S ) a 3 (Py-Br) a 2 ] and (CF 3 SO 3 ) 3 [Mo 6 I i 8 I a 1 (Py S ) a 4 (Py-Br) a 1 ]. Considering that one apical I a is attached to the cluster core, a (-3) charge must be carried by three triflate moieties in order to maintain the total charge balance.

Electrical transport

The I-V characteristics were measured at room temperature on the bare Hg // a-Cp Si(111) junction, after grafting of the pyridine-terminated monolayer and further immobilization of In the reverse bias regime, the current density increases after thermal grafting of pyridineterminated alkyl monolayer, but no further effect can be detected after the cluster immobilization step. This regime corresponds to injection of minority carriers (electrons) from p-type Si and transport through the a-C film.

Discussion

A monolayer of octahedral [Mo 6 I 8 ] 4+ cluster cores has been immobilized through complexation with a pyridine functionalized amorphous carbon surface. Each metallic cluster is bound to the organic monolayer surface through either three or four pyridine anchoring groups, From crystallographic data, the footprint of the [Mo 6 I 8 ] 4+ cluster core with bromopyridine complexation can be estimated at 2.2 nm 2 , i.e. the densely packed monolayer ideally represents 4.510 13 clusters / cm 2 . Experimentally, the Mo 6 I 8pyridinealkyla-C system shows a moderately large density of clusters ( Mo6I8 = 9.210 12 cm -2 ) providing a specific area of A mol = 10.9 nm 2 per bound cluster.

The binding energy of the main (Mo 3d 3/2 , Mo 3d 5/2 ) doublet is identical to that observed for

2 ABSTRACT

 2 Tetrahedral amorphous carbon (a-C) films have been grown by pulsed laser deposition to investigate a liquid phase process for surface immobilization of electroactive [Mo 6 I i 8 ] 4+ transition metal clusters cores using a complexation reaction with a pyridine-terminated alkyl monolayer covalently bonded to the a-C surface (Py S -alkyl / a-C). These films are stable against thermallyassisted grafting of alkene molecules and the covalent C-C interface provides a robust monolayer / a-C assembly. Octahedral [Mo 6 I i 8 ] 4+ cluster cores with iodine inner ligands and labile triflate apical ligands [Mo 6 I i 8 (CF 3 SO 3 ) a 6 ] 2-have been immobilized through partial complexation in apical position by surface pyridine groups (Py S ). Remaining CF 3 SO 3 -apical ligands of [Mo 6 I i 8 (Py S ) a y (CF 3 SO 3 ) a 6-y ] cluster units were further substituted with bromopyridine (Py-Br) to obtain air stable surface with expected final composition [Mo 6 I i 8 (Py S ) a y (Py-Br) a 6-y ]. The yield of the different reaction steps is followed by X-ray photoelectron spectroscopy, providing cluster coverage  Mo6I8 =910 12 cm -2 . Each [Mo 6 I 8 ] 4+ cluster is bound to the carbon surface through multiple anchoring metal sites (N PYR = 3 or 4), indicating that pyridine-terminated alkyl chains are flexible enough to accommodate four bonds. Electrical transport through Hg // Mo 6 I 8 -Py S -alkyl / a-C / p-Si(111) junctions show rectifying current-voltage characteristics but do not reveal any signature of cluster immobilization.

Scheme 1 .Scheme 2 . 6

 126 Scheme 1. Surface immobilization of a [Mo 6 I 8 ] 4+ metallic cluster core on amorphous carbon through complexation with a pyridine-terminated alkyl monolayer. For clarity, a single anchoring site is shown, however complexation with more than one anchoring site is also possible. In the final reaction step (Scheme 2), the remaining apical sites are substituted with bromopyridine.

8 2. 1 .

 81 cluster immobilization strategy (Scheme 1 and 2) is based on a complexation reaction of the electrically neutral precursor [(n-C 4 H 9 ) 4 N] 2 [Mo 6 I i 8 I a 6-x (CF 3 SO 3 ) a x ] in which a number N PYR of the labile triflate groups in apical position are displaced by the surface pyridine groups to form Mo-Py S bonding. To obtain derivatized surfaces sufficiently stable for subsequent characterizations, in a final step III, the (6-N PYR ) remaining triflate groups (CF 3 SO 3 ) are displaced by reaction with a more stable complexation moiety. Bromopyridine has been chosen because Br atoms are useful as labelling moieties to improve the XPS analysis accuracy. Electrical neutrality of the cluster monolayer requires the presence of counter ions, e.g tetrabutylammonium TBA + ((n-C 4 H 9 ) 4 N + ) (step I) or triflate CF 3 SO 3 -(step III). Owing to the octahedral geometry of the cluster and the steric hindrance of the pyridinealkyl chains bonded to the semiconductor surface, all six Mo atoms cannot be simultaneously attached to surface pyridine groups. Scheme 1 shows the minimal anchoring configuration where each cluster is linked to the surface through a single pyridine (Py S ) moiety. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT Pulsed laser deposition of amorphous carbon Carbon films (30 nm thick) were grown at room-temperature by pulsed laser deposition using a KrF laser (Tuilaser Excistar E20, = 248 nm, 2 Hz, 40 ns pulse width) in a turbo-pumped high vacuum chamber (base pressure ~ 10 -4 Pa). Crystalline silicon (c-Si (111), p-type) substrates were cleaned in ultrasonic bath (acetone, isopropanol, water) and dropped in NH 4 F (40%) to remove their native oxide before a-C film growth. They were placed at 60 mm from the rotating glassy carbon target (Sigradur G, HTW). The deposited carbon thickness is 0.03 nm / pulse for a pulse energy of 150 mJ (fluence 7.3 J.cm -2

  Molybdenum cluster-terminated carbon surfaces. Similar to our previous work with Si(111):H surfaces [35], our strategy for anchoring [Mo 6 I 8 ] 4+ cluster cores is based on the covalent derivatization of amorphous carbon surfaces by a complexing pyridine-terminated alkyl monolayer. Linear -functionalized alkenes are used to obtain densely packed monolayers with a robust covalent bonding to the a-C substrate [38]. The first part of this thermally-induced grafting process includes successive surface reactions [39]: (i) covalent grafting of C10 linear alkenes, -functionalized with ester moieties, (ii) conversion of terminal ester to reactive acid functionalities, (iii) amidation reaction with the amino-substituted pyridine. Briefly, ethyl undecylenate CH 2 =CH(CH 2 ) 8 -COOC 2 H 5 (Aldrich, 97%) was passed through a neutral, activated alumina column to remove residual water and peroxides, before liquid-phase reaction at 160 °C for at least 2 hours. Using a protected ester (rather than an acid) functionality offers the advantage of limiting the possible reactivity of acid functions with the A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 9 carbon surface. The terminal ester groups were further converted to acidic groups using dimethylformamide (DMF) + 0.25 M potassium tert-butoxyde (t-BuOK) for 10 min, followed by HCl 1M [40]. The acid-terminated surface was rinsed with ultrapure water and activated with Nhydroxysuccinimide (NHS) (Acros, 98+%) by immersion in a freshly prepared mixture of 1-(3dimethylaminopropyl)-3-ethylcarbodiimide (EDC) (Acros, 98+%) and NHS in DMF. The covalent attachment of the functional units was performed by immersing the NHS-activated molecular layer / a-C film in a deaerated acetonitrile solution containing 0.1 M of 4aminomethylpyridine (Aldrich, 98%) at room temperature for 3.5 hours.

  A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 10 Step III: to obtain derivatized surfaces sufficiently stable for subsequent characterization, the triflate anions remaining in apical position of the cluster after the immobilization step were substituted by 3-bromopyridine (Py-Br) by dipping the modified surface in neat 3-bromopyridine (99%, from Aldrich) for 30-40 min. Assuming that all apical triflates have been substituted and that electrically neutrality is provided by triflate counter anions, the expected final composition is (CF 3 SO 3 ) x-2 [Mo 6 I i 8 I a 6-x (Py S ) a y (Py-Br) a x-y ]. After this step III, in order to remove physisorbed metallic aggregates, a thorough rinsing of the Mo 6 cluster-modified surface with HPLC grade CH 2 Cl 2 (previously distilled over P 2 O 5 ) was performed [35].

1 )

 1 ) and inner ligands (I) to obtain their oxidation state, determination of the nature and number of ligands of the immobilized cluster [Mo 6 S ) a y (Py-Br) a x-y ] requires an accurate value of the bromine relative intensity. Once x has been determined using some iodine core level, the Br 3p or Br 3d core levels provide x-y, and hence the number of anchoring sites y = N PYR .Molybdenum Mo 3d (rather than Mo 3p) and sulphur S 2p (rather than S 2s) photoelectron emission lines were chosen for their larger photoemission cross section; in contrast, the iodine I 4d (rather than I 3d) line was used to avoid corrections for the dependence of the analyser sensitivity at small photoelectron kinetic energies. The bromine Br 3d intensity can be compared with the stronger Br 3p, although the latter are partially overlapped by the stronger I 4s band. The [Mo 6 I 8 ] 4+ cluster core coverage,  Mo6I8 (cm -2 ), was derived from the Mo 3d intensity through a normalization to the C1s intensity of a clean HOPG (highly oriented pyrolytic graphite, Neyco) substrate. Assuming that self-attenuation of the Mo signal by the cluster itself can be neglected, the density  Mo6I8 of [Mo 6 I 8 ] 4+ cluster cores (m = 6 Mo atoms) per unit surface: is derived from the measured peak areas A Mo and A HOPG (eV×counts.s -1 ), taking into account their relative photoemission cross sections (Mo 3d 5/2 ) / (C 1s) = 5.62, the C atom density (N HOPG = 11.4×10 22 at.cm -3 ) and the inelastic mean free path, HOPG s C1  = 1.78 nm, of C 1s photoelectrons (with 970 eV kinetic energy) in graphite [42]. A similar relationship applies for the coverage ( PYR ) of the pyridine-functionalized a-C surface, using the N 1s signal with (N 1s) / (C 1s) = 1.80, and m = 2 (pyridine and amide functions) if self-attenuation is neglected.

Fig. 1 .

 1 Fig. 1. XPS survey spectra ( = 0°) before (a) and after (b) immobilization of [Mo 6 I 8 ] 4+ cluster

14 3. 3 .

 143 Mo 6 I 8 -Pyridinealkyla-C assembly After attachment of molybdenum clusters and treatment with 3-bromopyridine, as expected the survey spectrum (Fig. 1b) shows the presence of iodine and molybdenum atoms characterized by their respective d levels (I 3d 3/2 , I 3d 5/2 , Mo 3d 3/2 , Mo 3d 5/2 and I 4d 5/2 at 632.5, 621.1, 232.8, 229.6 and 51.0 eV respectively) and 3p components (Mo 3p 3/2 at 395.4 eV).

Fig. 2 .Figure 2 Fig. 3 .

 223 Fig.2. XPS data ( = 0°, monochromatized X-ray source) showing the (I 3d 3/2 , I 3d 5/2 ) core level after [Mo 6 I 8 ] 4+ cluster immobilization. Some unreacted apical ligands are observed at smaller binding energy than the inner iodine ligands with an intensity ratio (I a / I i ) = 0.12.

In Figure 3 ,

 3 the strong (Mo 3d 3/2 , Mo 3d 5/2 ) doublet is observed with the expected 3.2 eV spin orbit splitting. The positions of the main Mo 3d 5/2 and 3d 3/2 peaks are identical to that observed for the starting product [(n-C 4 H 9 ) 4 N) 2 Mo 6 I 14 and are perfectly consistent with a Mo II oxidation state. In addition, a smaller component (indicated as Mo*, red arrows) is observed at larger binding energy (E ≈ 2.75 eV) with an intensity ratio (Mo * / Mo II ) = 0.23±0.05. Similarly,the broad Mo 3p level shows a peak tailing towards higher binding energies; as a consequence of the overlap between the N 1s and Mo 3p core levels (Fig.4), the fitted ratio (N1s / Mo 3p) = 1.7 is likely overestimated.

Fig. 4 .

 4 Fig. 4. XPS data ( = 45°) after [Mo 6 I 8 ] 4+ cluster core immobilization showing the (Mo 3p 1/2 , Mo

Fig. 5 .

 5 Fig. 5. XPS data ( = 45°) after [Mo 6 I 8 ] 4+ cluster immobilization showing the (Br 3d 3/2 , Br 3d 5/2 ) doublet. Besides the main peak (black line), a second component (red line) is observed at larger binding energy (E = 2.0 eV).

Fig. 6 .

 6 Fig. 6. XPS data ( = 45°) after [Mo 6 I 8 ] 4+ cluster core immobilization with fitted peaks which

6 )

 6 were used to estimate the average number of triflate anions which were not exchanged by surface pyridine (Py S ). (i) The Br 3d signal at 70 eV being broader than expected from the doublet separation (≈ 1 eV), data decomposition reveals the presence of two doublets separated by E ≈ 2.0 eV with an intensity ratio of ≈ 0.6±0.1. The ratio (Br 3d / I 4d) = 0.17 provides an average number of 1.5 bromopyridine units per bound metallic cluster. (ii) Decomposition of the near 183 eV also requires two (Br 3p 1/2 , Br 3p 3/2 ) doublets (Fig.6) with a total intensity consistent with the Br 3d fitting.As far as the counter anions are concerned, the C 1s spectrum shows a high binding energy (292.7 eV) component (not shown) assigned to CF 3 moieties pertaining to triflate anions (CF 3 SO 3 -) which remain on the surface after treatment with 3-bromopyridine. This result consistent with step III (Scheme 2) is confirmed by the presence of F 1s and S 2p signals at 684.2 eV and 169.0 eV, respectively.

Figure 6

 6 shows the (S 2p 1/2 , S 2p 3/2 ) doublet with the expected spin orbit separation of 1.37 eV and an intensity ratio of 0.58. Its position is consistent with triflate moieties[START_REF] Lindberg | Molecular Spectroscopy by Means of ESCA II. Sulfur compounds. Correlation of electron binding energy with structure[END_REF]

[Mo 6 I 8 ]

 68 4+ cluster core monolayer. Current density J(V) and numerically derived conductance 20 G(V) = (dJ/dV) characteristics were reproducible at several points of the sample, measured over several days.

Fig. 7 .

 7 Fig. 7. Current density (a) and conductance (b) characteristics of Hg // molecular layer / a-C / p-type Si junctions (T = 296 K): before (blue line) and after (green circles) grafting of a pyridineterminated alkyl monolayer to a-C ; after complexation of a monolayer of cluster cores (red triangles).

Figure 7

 7 Figure7shows strong rectification for all junctions with a rectification factor, J(+2V) / J(-

  where three triflate moieties are required to maintain the total charge balance.This work shows that [Mo 6 I 8 ] 4+ cluster cores are strongly bound to the pyridine functionalized carbon surface through multiple pyridine anchoring sites. Conversely, it might also indicate that clusters with fewer binding sites (being less stable) have been untied from the surface during the grafting process and that the complexation reaction is thus partly reversible. On the other hand, it cannot be excluded that the less stable cluster core configurations, e.g. with a low number of anchoring sites, have been removed during step III of the process.A large fraction (more than 60%) of available pyridine sites at the functionalized carbon surface ( PYR = 2.910 13 cm -2 ) are participating in cluster complexation, as derived from the average number of anchoring sites per cluster (N PYR = 3.5±0.4). Considering the topological constraints related to the octahedral cluster geometry, cluster attachment to the alkyl monolayer surface through 3 or 4 pyridine anchoring sites indicates that the pyridine-terminated alkyl chains are flexible enough to accommodate up to four bonds. This work confirms previous results for Mo 6 clusters immobilization on Si(111) where the number of anchoring groups was closer to 4 [35].

32 Highlights 1 /

 321 photo-hole generation.

  6 on the surface. However, in the case of Si substrates, it has been shown that

	quantitative XPS data analysis is hampered by the intense Si 2s plasmon losses (168 eV, 185 eV)
	overlapping both the S 2p (sulphur from triflate moieties), the I 4s (inner and apical ligands) and
	the Br 3p (bromine from the labelling bromopyridine groups) core level peaks [35]. Hence, the
	Mo 6 I 8 -pyridine -alkyl -a-C system provides better opportunity for a quantitative XPS analysis
	of the above chemical elements. In addition, the very low surface roughness (0.3-0.6 nm) of
	amorphous carbon [36] is also interesting for quantitative XPS analysis of the cluster coverage.
	Pulsed Laser Deposition (PLD) has been chosen to produce dense and smooth tetrahedral a-C
	films with a high sp 3 hybridization; since this sp 3 fraction does not exceed 60 % in the few nm
	from the surface, the main reactive chemical groups are probably C-H terminations and C=C
	double bonds [37]. a-C films can be reproducibly grafted with alkene molecules with no previous
	surface preparation, using the same thermally assisted process as used with hydrogenated
	crystalline Si(111):H surfaces [36], leading to the covalent immobilization of a dense alkyl
	monolayer (with ester or pyridine end-capping).
	The coverage and the composition of the grafted molecular layers are estimated from
	quantitative XPS analysis at different steps of the grafting process. The oxidation state, derived

) limits the occurrence of mercury diffusion through pinholes in the molecular layer. Current (I)

Acknowledgement

This work was supported by Agence Nationale de la Recherche (ANR-07-BLAN-0170-02, project "CLUSTSURF").

A C C E P T E D

M A N U S C R I P T


Minsu Cho

Suha Kwak

Cordelia Schmid

Jean Ponce

Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals

This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class. This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.

Introduction

Object localization and detection is highly challenging because of intra-class variations, background clutter, and occlusions present in real-world images. While significant progress has been made in this area over the last decade, as shown by recent benchmark results [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF]16], most state-ofthe-art methods still rely on strong supervision in the form of manually-annotated bounding boxes on target instances. Since those detailed annotations are expensive to acquire and also prone to unwanted biases and errors, recent work has explored the problem of weakly-supervised object dis-Figure 1. Unsupervised object discovery in the wild. We tackle object localization in an unsupervised scenario without any type of annotations, where a given image collection may contain multiple dominant object classes and even outlier images. The proposed method discovers object instances (red bounding boxes) with their distinctive parts (smaller boxes). (Best viewed in color.) covery where instances of an object class are found in a collection of images without any box-level annotations. Typically, weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Nguyen | Weakly supervised discriminative localization and classification: a joint learning process[END_REF][START_REF] Pandey | Scene recognition and weakly supervised object localization with deformable part-based models[END_REF][START_REF] Shi | Bayesian joint topic modelling for weakly supervised object localisation[END_REF][START_REF] Siva | In defence of negative mining for annotating weakly labelled data[END_REF][START_REF] Wang | Weakly supervised object localization with latent category learning[END_REF] requires positive and negative image-level labels for a target object class. On the other hand, cosegmentation [START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF][START_REF] Kim | Distributed cosegmentation via submodular optimization on anisotropic diffusion[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] and colocalization [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF]51] assume less supervision and only require the image collection to contain a single dominant object class, allowing noisy images to some degree.

This paper addresses unsupervised object localization in a far more general scenario where a given image collection contain multiple dominant object classes and even noisy images without any target objects. As illustrated in Fig. 1, the setting of this problem is fully unsupervised, without any image-level annotations, an assumption of a single dominant class, or even a known number of object classes. In spite of this generality, the proposed method markedly outperforms the state of the arts in colocalization [START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF]51] on standard benchmarks [16,[START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF], and closely competes with current weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Shi | Bayesian joint topic modelling for weakly supervised object localisation[END_REF][START_REF] Wang | Weakly supervised object localization with latent category learning[END_REF].

We advocate a part-based matching approach to unsupervised object discovery using bottom-up region proposals. Multi-scale region proposals have been widely used before to restrict the search space for object bounding boxes in ob-ject recognition [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF][START_REF] Uijlings | Selective search for object recognition[END_REF] and localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF]51,[START_REF] Vicente | Object cosegmentation[END_REF]. We go further and propose here to use these regions to form a set of candidate regions not only for objects, but also for object parts. We use a probabilistic Hough transorm [START_REF] Ballard | Generalizing the Hough transform to detect arbitrary shapes[END_REF] to match those candidate regions across images, and assign them confidence scores reflecting both appearance and spatial consistency. This can be seen as an unsupervised and efficient variant of both deformable part models [18,[START_REF] Felzenszwalb | Pictorial structures for object recognition[END_REF] and graph matching methods [START_REF] Cho | Learning graphs to match[END_REF][START_REF] Duchenne | A graph-matching kernel for object categorization[END_REF]. Objects are discovered and localized by selecting the most salient regions that contain corresponding parts. To this end, we introduce a score that measures how much a region stands out over other regions containing it. The proposed algorithm alternates between part-based region matching and foreground localization, improving both over iterations.

The main contributions of this paper can be summarized as follows: (1) A part-based region matching approach to unsupervised object discovery is introduced. (2) An efficient and robust matching algorithm based on a probabilistic Hough transform is proposed. (3) A standout score for robust foreground localization is introduced. (4) Object discovery and localization in a fully unsupervised setup is explored on challenging benchmark datasets [16,[START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF].

Related work

Unsupervised object discovery has long been attempted in computer vision. Sivic et al. [START_REF] Sivic | Discovering object categories in image collections[END_REF] and Russell et al. [START_REF] Russell | Using multiple segmentations to discover objects and their extent in image collections[END_REF] apply statistical topic discovery models. Grauman and Darrel [START_REF] Grauman | Unsupervised learning of categories from sets of partially matching image features[END_REF] use partial correspondence and clustering of local features. Kim and Torralba [START_REF] Kim | Unsupervised detection of regions of interest using iterative link analysis[END_REF] employ a link analysis technique. Faktor and Irani [START_REF] Faktor | Clustering by composition" -unsupervised discovery of image categories[END_REF] propose clustering by composition. Unsupervised object discovery, however, has proven extremely difficult "in the wild"; all of these previous approaches have been successfully demonstrated in a restricted setting with a few distinctive object classes, but their localization results turn out to be far behind weaklysupervised results on challenging benchmarks [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Kim | Unsupervised detection of regions of interest using iterative link analysis[END_REF]51].

Given the difficulty of fully unsupervised discovery, recent work has more focused on weakly-supervised approaches from different angles. Cosegmentation is the problem of segmenting common foreground regions out of a set of images. It has been first introduced by Rother et al. [START_REF] Rother | Cosegmentation of image pairs by histogram matching -incorporating a global constraint into MRFs[END_REF] who fuse Markov random fields with color histogram matching to segment objects common to two images. Since then, this approach has been improved in numerous ways [START_REF] Batra | icoseg: Interactive co-segmentation with intelligent scribble guidance[END_REF][START_REF] Cho | Co-recognition of image pairs by data-driven Monte Carlo image exploration[END_REF][START_REF] Hochbaum | An efficient algorithm for co-segmentation[END_REF]55], and extended to handle more general cases [START_REF] Cho | Unsupervised detection and segmentation of identical objects[END_REF][START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF][START_REF] Vicente | Object cosegmentation[END_REF]. Given the same type of input as cosegmentation, colocalization seeks to localize objects with bounding boxes instead of pixel-wise segmentations. Tang et al. [51] use the discriminative clustering framework of [START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF] to localize common objects in a set of noisy images, and Joulin et al. [START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF] extend it to colocalization of video frames. Weakly-supervised localiza-tion [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Nguyen | Weakly supervised discriminative localization and classification: a joint learning process[END_REF][START_REF] Pandey | Scene recognition and weakly supervised object localization with deformable part-based models[END_REF][START_REF] Siva | Looking beyond the image: Unsupervised learning for object saliency and detection[END_REF][START_REF] Song | On learning to localize objects with minimal supervision[END_REF] shares the same type of output as colocalization, but assumes a more supervised scenario with image-level labels that indicate whether a target object class appears in the image or not. These labels enable to learn more discriminative localization methods, e.g., by mining negative images [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF]. Recent work on discriminative patch discovery [START_REF] Endres | Learning collections of part models for object recognition[END_REF][START_REF] Singh | Unsupervised discovery of mid-level discriminative patches[END_REF][START_REF] Sun | Learning discriminative part detectors for image classification and cosegmentation[END_REF] learns mid-level visual representations in a weakly-supervised mode, and use them for object recognition [START_REF] Endres | Learning collections of part models for object recognition[END_REF][START_REF] Singh | Unsupervised discovery of mid-level discriminative patches[END_REF] and discovery [START_REF] Doersch | Context as supervisory signal: Discovering objects with predictable context[END_REF][START_REF] Sun | Learning discriminative part detectors for image classification and cosegmentation[END_REF].

Region proposals have been used in many of the methods discussed so far, but most of them [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF][START_REF] Kim | Unsupervised detection of regions of interest using iterative link analysis[END_REF][START_REF] Russell | Using multiple segmentations to discover objects and their extent in image collections[END_REF]51,[START_REF] Vicente | Object cosegmentation[END_REF] use relatively a small number of the best proposals (typically, less than 100 for each image) to form whole object hypotheses, often together with generic objectness measures [START_REF] Alexe | Measuring the objectness of image windows[END_REF]. In contrast, we use a large number of region proposals (typically, between 1000 and 4000) as primitive elements for matching without any objectness priors. While many other approaches [START_REF] Cho | Unsupervised detection and segmentation of identical objects[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF][START_REF] Rubio | Unsupervised co-segmentation through region matching[END_REF] also use correspondences between image pairs to discover object regions, they do not use an efficient part-based matching approach such as ours. Many of them [START_REF] Cho | Unsupervised detection and segmentation of identical objects[END_REF][START_REF] Grauman | Unsupervised learning of categories from sets of partially matching image features[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] are driven by correspondence techniques, e.g., the SIFT flow [START_REF] Liu | SIFT Flow: dense correspondence across scenes and its applications[END_REF], based on generic local regions. In the sense that semi-local or mid-level parts are crucial for representing generic objects [18,[START_REF] Lazebnik | Semi-local affine parts for object recognition[END_REF], we believe segment-level regions are more adequate for object matching and discovery. The work of Rubio et al. [START_REF] Rubio | Unsupervised co-segmentation through region matching[END_REF] introduces such a segment-level matching term in their cosegmentation formulation. Unlike ours, however, it requires a reasonable initialization by an objectness measure [START_REF] Alexe | Measuring the objectness of image windows[END_REF], and does not scale well with a large number of segments and images.

Proposed approach

For unsupervised object discovery, we combine an efficient part-based matching technique with a foreground localization scheme. In this section we first introduce the two main components of our approach, and then describe the overall algorithm for unsupervised object discovery.

Part-based region matching

For part-based matching in an unsupervised setting, we use off-the-shelf region proposals [START_REF] Manen | Prime object proposals with randomized Prim's algorithm[END_REF] as candidate regions for objects and object parts: Diverse multi-scale proposals include meaningful parts of objects as well as objects themselves. Let us assume that two sets of region proposals R and R have been extracted from two images I and I , respectively. Let r = (f, l) ∈ R denote a region with feature f observed at location l. We use 8 × 8 HOG descriptors [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF][START_REF] Hariharan | Discriminative decorrelation for clustering and classification[END_REF] for f to describe the region patches, and center position and scale for l to specify the location. A match between r and r is represented by (r, r ). For the sake of brevity, we use short notations D for two region sets, and m for a match: D = (R, R ), m = (r, r ) in R × R . Then, our probabilistic model of a match confidence for m is rep- (d) Heat map representation of region confidences. Figure 2. Part-based region matching using bottom-up region proposals. (a-b) Given two images and their multi-scale region proposals [START_REF] Manen | Prime object proposals with randomized Prim's algorithm[END_REF], the proposed matching algorithm efficiently evaluates candidate matches between two sets of regions (2205×1044 regions in this example) and produce match confidences for them. (c) Based on the match confidence, the 20 best matches are shown by greedy mapping with a one-to-one constraint. The confidence is color-coded in each match (red: high, blue: low). (d) The region confidences of Eq.( 4) are visualized in the heat map representation. Common object foregrounds tend to have higher confidences than others. (Best viewed in color.) resented by p(m|D). Assuming a common object appears in I and I , let the offset x denote its pose displacement between I and I , related to properties such as position and scale change. p(x|D) becomes the probability of the common object being located with offset x. Now, the match confidence is decomposed in a Bayesian manner:

p(m|D) = x p(m, x|D) = x p(m|x, D)p(x|D) = p(m a ) x p(m g |x)p(x|D), (1)
where we suppose that appearance matching m a is independent of geometry matching m g and an object location offset x. Appearance likelihood p(m a ) is simply computed as the similarity between f and f . Geometry likelihood p(m g |x) is estimated by comparing displacement l -l to the given offset x. For p(m g |x), we construct three-dimensional offset bins for translation and scale change, and use a Gaussian distribution centered on offset x.

The main issue is how to estimate geometry prior p(x|D) without any information about objects and their locations. Inspired by the generalized Hough transform [START_REF] Ballard | Generalizing the Hough transform to detect arbitrary shapes[END_REF] and its extensions [START_REF] Leibe | Robust object detection with interleaved categorization and segmentation[END_REF][START_REF] Zhang | Efficient kernels for identifying unbounded-order spatial features[END_REF], we propose the Hough space score h(x|D), that is the sum of individual probabilities p(m, x|D) over all possible region matches m ∈ R × R . The voting is done with an initial assumption of a uniform prior over x:

h(x|D) = m p(m|x, D) = m p(m a )p(m g |x), (2)
which predicts a pseudo likelihood of common objects at offset x. Assuming p(x|D) ∝ h(x|D)1 , we rewrite Eq.( 1) to define the Hough match confidence as

c(m|D) = p(m a ) x p(m g |x)h(x|D). (3)
Interestingly, this formulation can be seen as a combination of bottom-up and top-down processes: The bottom-up process aggregates individual votes into the Hough space scores (Eq.( 2)), and the top-down process evaluates each match confidence based on those scores (Eq.( 3)). We call this algorithm Probabilistic Hough Matching (PHM).

Leveraging the Hough space score as a spatial prior, it provides robust match confidences for candidate matches. In particular, given multi-scale region proposals, different region matches on the same object cast votes for each other, and make all the region matches on the object obtain high confidences. This is an efficient part-based matching procedure with complexity of O(nn ), where n and n are the number of regions in R and R , respectively. As shown in Fig. 2c, reliable matches can be obtained when a proper mapping constraint (e.g., one-to-one, one-to-many, etc.) is enforced on the confidence as a post-processing. 2We define the region confidence as a max-pooled match confidence for r in R with respect to R :

φ(r) = max r c (r, r )|(R, R ) , (4)
which derives from the best matches from R to R under one-to-many mapping constraints. High region confidences guarantee that corresponding regions have at least single good matches in consideration of both appearance and spatial consistency. As shown in Fig. 2d, the region confidence provides a useful measure for common regions between images, thus functioning as a driving force in object discovery.

Foreground localization

Foreground objects do not directly emerge from partbased region matching: A region with the highest confidence is often a salient part of a common object while good localization is supposed to tightly bound the entire object region. We need a principled and unsupervised way to tackle the intrinsic ambiguity in separating the foreground objects from the background, which is one of the main challenges in unsupervised object discovery. In Gestalt principles of visual perception [START_REF] Rubin | Figure and ground[END_REF] and design [START_REF] Jackson | Gestalt-a learning theory for graphic design education[END_REF], regions that "stand out" are more likely to be seen as a foreground. A high contrast lies between the foreground and background, and a lower contrast between foreground parts or background parts. Inspired by these figure/ground principles, we evaluate a foreground score of a region by its perceptual contrast standing out of its potential backgrounds. To measure the contrast, we leverage on the region confidence from partbased matching, which is well supported by the work of Peterson and Gibson, demonstrating the role of object recognition or matching in the figure/ground process [START_REF] Peterson | Object recognition contributions to figure-ground organization: Operations on outlines and subjective contours[END_REF].

First, we generalize the notion of the region confidence to exploit multiple images. Let us assume I as a target image, and I as a source image. The region confidence of Eq.( 4) is a function of region r in target R with its best correspondence r in source R as a latent variable. Given multiple source images, it can be naturally extended with more latent variables, meaning the best correspondences from the source images to r. Let us define neighbor images N of target image I as an index set of source images where an object in I may appear. Generalizing Eq.( 4), the region confidence can be rewritten as

ψ(r) = max {r i } i∈N i∈N c (r, r i )|(R, R i ) = i∈N max r ∈R i c (r, r )|(R, R i ) , (5)
which reduces to the aggregated confidence from the neighbor images. More images may give better confidences.

Given regions R with their region confidences, we evaluate a perceptual contrast for region r ∈ R by computing the increment of its confidence from its potential backgrounds. To this end, we define the standout score as The standout score reflects the principle that we perceive a lower contrast between parts of the foreground than that between the background and the foreground. As shown in the example of Fig. 3b, we can localize potential object regions by selecting regions with top standout scores.

s(r) = ψ(r) -max rb∈B(r) ψ(r b ), s.t. B(r) = {r b | r r b , r b ∈ R}, (6)

Object discovery algorithm

For unsupervised object discovery, we combine partbased region matching and foreground localization in a coordinate descent-style algorithm. Given a collection of images C, our algorithm alternates between matching image pairs and re-localizing potential object regions. Instead of matching all possible pairs over the images, we retrieve k neighbors for each image and perform part-based matching only from those neighbor images. To make the algorithm robust to localization failure in precedent iterations, we maintain five potential object regions for each image. Both the neighbor images and the potential object regions are updated over iterations.

The algorithm starts with an entire image region as an initial set of potential object regions O i for each image I i , and performs the following three steps at each iteration.

Neighbor image retrieval. For each image I i , k nearest neighbor images {I j | i ∈ N i } are retrieved based on the similarity between O i and O j . We use 10 neighbor images (k = 10). 3 At the first iteration, as the potential object regions become entire image regions, nearest-neighbor matching with the GIST descriptor [START_REF] Torralba | Small codes and large image databases for recognition[END_REF] is used. From the second iteration, we perform PHM with re-localized object regions. For efficiency, we only use the top 20 region proposals according to region confidences, which are contained in the potential object regions. The similarity for retrieval is computed as the sum of those region confidences.

Part-based region matching. Part-based matching by PHM is performed on I i from its neighbor images {I j | j ∈ N i }. To exploit current localization in a robust way, an asymmetric matching strategy is adopted: We use all regions proposals in I i , whereas for the neighbor image I j we take regions only contained in potential object regions O j . This matching strategy does not restrict potential object regions in target I i while effectively utilizing localized object regions at the precedent step.

Foreground localization. For each image I i , the standout scores are computed so that the set of potential object regions O i is updated to that of regions with top standout scores. This re-localization improves both neighbor image retrieval and region matching at the subsequent iteration.

These steps are repeated for a few iterations until nearconvergence. As will be shown in our experiments, 5 iterations are sufficient as no significant change occurs in more iterations. Final localization is done by selecting the most standing-out region at the end. The algorithm is designed based on the idea that better localization makes better retrieval and matching, and vice versa. As each image is independently processed at each iteration, the algorithm is easily parallelizable in computation. Object discovery on 500 images takes less than an hour with a 10-core desktop computer, using our current parallel MATLAB implementation.

Experimental evaluation

The degree of supervision used in visual learning tasks varies from strong (supervised localization [18,[START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF]) to weak (weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Siva | Looking beyond the image: Unsupervised learning for object saliency and detection[END_REF]), very weak (colocalization [START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF]51] and cosegmentation [START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF]), and null (fully-unsupervised discovery). To evaluate our approach for unsupervised object discovery, we conduct two types of experiments: separate-class and mixed-class experiments.

Our separate-class experiments test performance of our approach in a very weakly supervised mode. Our mixedclass experiments test object discovery "in the wild" (in a fully-unsupervised mode), by mixing all images of all classes in a dataset, and evaluating performance on the whole dataset. To the best of our knowledge, this type of localization experiments has never been fully attempted before on challenging real-world datasets. We conduct experiments on two realistic benchmarks, the Object Discovery [START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] and the PASCAL VOC 2007 [16], and compare the results with those of the current state of the arts in cosegmentation [START_REF] Kim | Distributed cosegmentation via submodular optimization on anisotropic diffusion[END_REF][START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF][START_REF] Joulin | Multi-class cosegmentation[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF], colocalization [START_REF] Chum | An exemplar model for learning object classes[END_REF][START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Russell | Using multiple segmentations to discover objects and their extent in image collections[END_REF][START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF]51], and weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Nguyen | Weakly supervised discriminative localization and classification: a joint learning process[END_REF][START_REF] Pandey | Scene recognition and weakly supervised object localization with deformable part-based models[END_REF][START_REF] Siva | Looking beyond the image: Unsupervised learning for object saliency and detection[END_REF][START_REF] Wang | Weakly supervised object localization with latent category learning[END_REF].

Evaluation metrics

The correct localization (CorLoc) metric is an evaluation metric widely used in related work [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF][START_REF] Siva | Looking beyond the image: Unsupervised learning for object saliency and detection[END_REF]51], and defined as the percentage of images correctly localized according to the PASCAL criterion: area(bp∩bgt) area(bp∪bgt) > 0.5, where b p is the predicted box and b gt is the ground-truth box. The metric is adequate for a conventional separateclass setup: As a given image collection contains a single target class, only object localization is evaluated per image. In a mixed-class setup, however, we have another dimension involved: As different images may contain different object classes, associative relations across the images need to be evaluated. As such a metric orthogonal to CorLoc, we propose the correct retrieval (CorRet) evaluation metric defined as follows. Given the k nearest neighbors identified by retrieval for each image, CorRet is defined as the mean percentage of these neighbors that belong to the same (groundtruth) class as the image itself. This measure depends on k, fixed here to a value of 10. CorRet may also prove useful in other applications that discover the underlying "topology" (nearest-neighbor structure) of image collections. CorRet and CorLoc metrics effectively complement each other in the mixed-class setup: CorRet reveals how correctly an image is associated to other images, while CorLoc measures how correctly an object is localized in the image.

The Object Discovery dataset

The Object Discovery dataset [START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] was collected by the Bing API using queries for airplane, car, and horse, resulting in image sets containing outlier images without the query object. We use the 100 image subsets [START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] to enable comparisons to previous state of the art in cosegmentation and colocalization. In each set of 100 images, airplane, car, horse have 18, 11, 7 outlier images, respectively. Following [51], we convert the ground-truth segmentations and cosegmentation results of [START_REF] Kim | Distributed cosegmentation via submodular optimization on anisotropic diffusion[END_REF][START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF][START_REF] Joulin | Multi-class cosegmentation[END_REF][START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF] to localization boxes.

We conduct separate-class experiments as in [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF]51], and a mixed-class experiment on a collection of 300 images from all the three classes. The mixed-class image collection contains 3 classes and 36 outlier images. Figure 4 shows the average CorLoc and CorRet over iterations, where we see the proposed algorithm quickly improves both localization (CorLoc) and retrieval (CorRet) in early iterations, and then approaches a steady state. In the separate-class setup, Cor-Ret is always perfect because no other object class exists in the retrieval. As we have found no significant change in both localization and retrieval after 4-5 iterations in all our experiments, we measure all performances of our method in this paper after 5 iterations. The separate-class results are quantified in Table 1, and compared to those of state-of-theart cosegmentation [START_REF] Kim | Distributed cosegmentation via submodular optimization on anisotropic diffusion[END_REF][START_REF] Joulin | Discriminative clustering for image co-segmentation[END_REF][START_REF] Joulin | Multi-class cosegmentation[END_REF] and colocalization [START_REF] Rubinstein | Unsupervised joint object discovery and segmentation in internet images[END_REF]51] methods. Our method outperforms all of them in this setup. The mixed-class result is in Table 2, and examples of localization are shown in Fig. 5. Remarkably, our localization performance in the mixed-class setup is almost the same as that in the separate-class setup. Localized objects are visualized in red boxes with five most confident regions inside the object, indicating parts most contributing to discovery. Table 2 and Fig. 4 show that our localization is robust to noisy neighbor images retrieved from different classes.

PASCAL VOC 2007 dataset

The PASCAL VOC 2007 [16] contains realistic images of 20 object classes. Compared to the Object Discovery dataset, it is significantly more challenging due to considerable clutter, occlusion, and diverse viewpoints. To facilitate a scale-level analysis and comparison to other methods, we conduct experiments on two subsets of different sizes: PASCAL07-6x2 and PASCAL07-all. The PASCAL07-6x2 subset [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF] consists of all images from 6 classes (aeroplane, bicycle, boat, bus, horse, and motorbike) of train+val dataset from the left and right aspect each. Each of the 12 class/viewpoint combinations contains between 21 and 50 images for a total of 463 images. For a large-scale experiment with all classes following [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Pandey | Scene recognition and weakly supervised object localization with deformable part-based models[END_REF], we take all train+val dataset images discarding images that only contain object instances marked as "difficult" or "truncate". Each of the 20 classes contains between 49 and 1023 images for a total of 4548 images. We refer to it as PASCAL07-all.

Experiments on PASCAL07-6x2. In the separate-class setup, we evaluate performance for each class in Table 3, where we also analyze each component of our method by removing it from the full version: 'w/o MOR' eliminates the use of multiple object regions over iterations, thus main- As expected, we can see that the removal of each component damages performance substantially. In particular, it clearly shows both part-based matching (using PHM) and foreground localization (using the standout score) are crucial for robust object discovery. In Table 5, we quantitatively compare ours to previous results [START_REF] Chum | An exemplar model for learning object classes[END_REF][START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Siva | Weakly supervised object detector learning with model drift detection[END_REF]51] on PASCAL07-6x2. Our method significantly outperforms those with a large margin. Note that our method does not incorporate any form of object priors such as off-theshelf objectness measures [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Siva | Weakly supervised object detector learning with model drift detection[END_REF]51], and only use positive images (P) without more training data, i.e., negative images (N) [START_REF] Deselaers | Localizing objects while learning their appearance[END_REF][START_REF] Siva | Weakly supervised object detector learning with model drift detection[END_REF]. For the mixed-class experiment, we run our method on a collection of all class/view images in PASCAL07-6x2, and evaluate its CorLoc and CorRet peformance in Table 4. To better understand our retrieval performance per class, we measure CorRet for classes (regardless of views) in the third row, and analyze it by increasing the numbers of iterations and neighbor images in Fig. 6. This shows that our method achieves better localization and retrieval simultaneously, and benefits from each other. In Fig. 7, we show example results of our mixed-class experiment on PASCAL07-6x2. In spite of a small size of objects even partially occluded, our method is able to localize instances from cluttered scenes, and discovers confident object parts as well. From Table 5, we see that even without using the separate-class setup, the method localizes target objects markedly better than recent colocalization methods.

Experiments on PASCAL07-all. Here we tackle a much more challenging and larger-scale discovery task, using all the images from the PASCAL07 dataset. We first report separate-class results, and compare our results to those of the state of the arts in weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF][START_REF] Pandey | Scene recognition and weakly supervised object localization with deformable part-based models[END_REF][START_REF] Shi | Bayesian joint topic modelling for weakly supervised object localisation[END_REF][START_REF] Siva | Weakly supervised object detector learning with model drift detection[END_REF][START_REF] Siva | In defence of negative mining for annotating weakly labelled data[END_REF][START_REF] Siva | Looking beyond the image: Unsupervised learning for object saliency and detection[END_REF][START_REF] Wang | Weakly supervised object localization with latent category learning[END_REF] and colocalization [START_REF] Joulin | Efficient image and video co-localization with frank-wolfe algorithm[END_REF] in Table 6. Note that weakly-supervised methods use more training data, i.e., negative images (N). Also note that the best performing method [START_REF] Wang | Weakly supervised object localization with latent category learning[END_REF] uses CNN features pretrained on the ImageNet dataset [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF], thus additional supervised data (A). Surprisingly, the performance of our method is very close to the best of weakly-supervised localization [START_REF] Cinbis | Multi-fold MIL training for weakly supervised object localization[END_REF] not using such additional data.

In the mixed-class setting, we face an issue linked to the potential presence of multiple dominant labeled (groundtruth) objects in each image. Basically, both CorLoc and CorRet are defined as a per-image measure, e.g., CorLoc assigns an image true if any true localization is done in the image. For images with multiple class labels in the mixedclass setup, which is the case of PASCAL-all with highly overlapping class labels (e.g., persons appear in almost 1/3 of images), CorLoc needs to be extended in a natural manner. To measure a class-specific average CorLoc in such a multi-label and mixed-class setup, we take all images containing the object class and measure their average CorLoc for the class. The upper bound of this class-specific average CorLoc may be less than 100% because only one localization exists for each image in our setting. To complement this, as shown at the last column of Table 7, we add the 'any'-class average CorLoc, where we assign an image true if any true localization of any class exists in the image. The similar evaluation is also done for Cor-Rec. Both 'any'-class CorLoc and CorRet have an upper bound of 100% even when images have multiple class labels, whereas those in 'Av.' (average) may not. Note that the mixed-class PASCAL07-all dataset has a very imbalanced class distribution: the 20 classes have very different numbers of images, from 49 (sheep) to 1023 (person). Yet, as quantified in Table 7, our method still performs well even in this unsupervised mixed-class setting, and its localization performance is comparable to that in the separate-class setup. To better understand this, we visualize in Fig. 8 a confusion matrix of retrieved neighbor images based on the mixed-class result, where each row corresponds to the average retrieval ratios (%) by each class. Note that the matrix reflects class frequency so that the person class appears dominant. We clearly see that despite relatively low retrieval accuracy, many of retrieved images come from other classes with partial similarity, e.g., bicycle -motorbike, bus -car, etc. Figure 9 shows a typical example of such cases. These results strongly suggest that our part-based approach to object discovery effectively benefits from different but similar classes without any class-specific supervision. Interestingly, the significant difference in retrieval performance (CorRet) from 100% in the separateclass setup influences much less on localization (CorLoc). Further analysis of our experiments also reveals that in the case of an imbalanced distribution of classes, a class with lower frequency is harder to be localized than a class with higher frequency. To see this, consider 'the highest' (person, car, chair, dog, cat) and 'the lowest' (sheep, cow, boat, bus, dinningtable) in class frequency. We have measured how much the average performance changes between the separate-class (clean) and mixed-class (imbalanced) settings. The average CorLoc of 'the highest' only drops by 1.2%, while that of 'the lowest' drops by 9.4%. This clearly indicates that a class with lower class frequency is harder to localize in the mixed-class setting. Retrieval performance of 'the lowest' (CorRet 11.0%) is also much worse than that of 'the highest' (CorRet 30.7%). For more information, see our project webpage: http://www.di.ens. fr/willow/research/objectdiscovery/.

Discussion and conclusion

We have demonstrated unsupervised object localization in the challenging mixed-class setup, which has never been fully attempted before on a challenging dataset such as [16]. The result shows that the effective use of part-based matching is a crucial factor for object discovery. In the future, we will advance this direction and further explore how to handle multiple object instances per image as well as build visual models for classification and detection. In this paper, our aim has been to evaluate our unsupervised algorithm per se, and have thus abstained from any form of additional supervision such as off-the-shelf saliency/objectness measures, negative data, and pretrained features. The use of such information will further improve our results.

  (a) Input images. (c) Top 20 region matches. (b) Bottom-up region proposals.

  (a) Region confidences with respect to multiple source images. (b) Measuring the standout score from the region confidences.

Figure 3 .

 3 Figure 3. Foreground localization. (a) Given multiple source images with common objects, region confidences can be computed according to Eq.(5). More source images may give better region confidences. (b) Given regions (boxes) on the left, the standout score of Eq.(6) for the red box corresponds to the difference between its confidence and the maximum confidence of boxes containing the red box (green boxes). In the same way, the standout score for the white box takes into account blue, red, and green boxes altogether as its potential backgrounds. Three boxes on the right are ones with the top three standout scores from the region confidence. The red one has the top score. (Best viewed in color.)where r r b means region r is contained in region r b . The idea is illustrated in Fig.3b. Imagine a region gradually shrinking from a whole image region, to a tight object region, to a part region. Significant increase in region confidence is most likely to occur at the point of taking the tight object region. In practice, we decide the inclusive relation r r b by two simple criteria: (1) The box area of r is less than 50% of the box area of r b . (2) 80% of the box area of r overlaps with the box area of r b .The standout score reflects the principle that we perceive a lower contrast between parts of the foreground than that between the background and the foreground. As shown in the example of Fig.3b, we can localize potential object regions by selecting regions with top standout scores.

Figure 4 .

 4 Figure 4. Average CorLoc (left) and CorRet (right) vs. # of iterations on the Object Discovery dataset.

Figure 5 .

 5 Figure 5. Examples of localization on unlabeled Object Discovery dataset. Small boxes inside the localized object box (red box) represents five most confident part regions. (Best viewed in color.)

Figure 6 .

 6 Figure 6. CorRet variation on mixed-class PASCAL07-6x2.

Figure 7 .

 7 Figure 7. Example results on mixed-class PASCAL07-6x2.

Figure 8 .

 8 Figure 8. Confusion matrix of retrieval on mixed PASCAL07-all.

Figure 9 .

 9 Figure 9. Localization in an example and its neighbor images on mixed-class PASCAL07-all. A bus is successfully localized in the image (red dashed box) from its neighbors (10 images) containing even other classes (car, sofa). Boxes in the neighbors show potential objects at the final iteration. (Best viewed in color.)

Table 1 .

 1 CorLoc (%) on separate-class Object Discovery dataset.

	Methods	Airplane Car Horse Average
	Kim et al. [29]	21.95	0.00 16.13	12.69
	Joulin et al. [25]	32.93	66.29 54.84	51.35
	Joulin et al. [26]	57.32	64.04 52.69	58.02
	Rubinstein et al. [40]	74.39	87.64 63.44	75.16
	Tang et al. [51]	71.95	93.26 64.52	76.58
	Ours	82.93	94.38 75.27	84.19

Table 2 .

 2 Performance on mixed-class Object Discovery dataset.

	Evaluation metric	Airplane	Car	Horse Average
	CorLoc	81.71	94.38 70.97	82.35
	CorRet	73.30	92.00 82.80	82.70

Table 5 .

 5 CorLoc comparison on PASCAL07-6x2.

	Method	Data used	Avg. CorLoc (%)
	Chum and Zisserman [8]	P + N	33
	Deselaers et al. [12]	P + N	50
	Siva and Xiang [47]	P + N	49
	Tang et al. [51]	P	39
	Ours	P	68
	Ours (mixed-class)	unsupervised	54

Table 3 .

 3 CorLoc performance (%) on separate-class PASCAL07-6x2 .79 77.08 62.00 25.00 32.56 66.67 91.30 83.33 86.96 82.96 70.59 67.68 Ours w/o MOR 62.79 74.36 52.08 42.00 15.91 27.91 61.90 91.30 85.42 76.09 48.72 8.82 53.94 Ours w/o PHM 39.53 38.46 54.17 60.00 6.82 9.30 42.86 73.91 68.75 82.61 33.33 2.94 42.72

		aeroplane	bicycle		boat	bus		horse		motorbike
	Method	L	R	L	R	L	R	L	R	L	R	L	R	Average
	Ours (full) 62.79 71Ours w/o STO 34.88 0.0	2.08	0.0	0.0	4.65	0.0	8.70 64.58 30.43 2.56	0.0	12.32
		Table 4. CorLoc and CorRet performance (%) on mixed-class PASCAL07-6x2.
		aeroplane	bicycle		boat	bus	horse		motorbike
	Metric	L	R	L	R	L	R	L	R	L	R	L	R	Average
	CorLoc	62.79 66.67 54.17 56.00 18.18 18.60 42.86 69.57 70.83 71.74 69.23 44.12	53.73
	CorRet	61.40 42.56 48.75 56.80 19.09 13.02 13.33 30.87 41.46 41.74 38.72 43.24	37.58
	CorRet (class)	74.39		72.35	29.43	44.32	52.66		59.04	55.36
	taining only a single potential object region for each im-
	age. 'w/o PHM' substitutes PHM with appearance-based
	matching without any geometric consideration. 'w/o STO'
	replaces the standout score with the maximum confidence.

Table 6 .

 6 CorLoc (%) on separate-class PASCAL07-all, compared to the state of the arts in weakly-supervised / co-localization. Method Data used aero bicy bird boa bot bus car cat cha cow dtab dog hors mbik pers plnt she sofa trai tv Av. Pandey & Lazebnik [36] P + N 50.9 56.7 -10.6 0 56.6 --2.5 -14.3 -50.0 53.5 11.2 5.0 -34.9 33.0 40.6 -Siva & Xiang [47] P + N 42.4 46.5 18.2 8.8 2.9 40.9 73.2 44.8 5.4 30.5 19.0 34.0 48.8 65.3 8.2 9.4 16.7 32.3 54.8 5.5 30.4 Siva et al. [45] P + N 45.8 21.8 30.9 20.4 5.3 37.6 40.8 51.6 7.0 29.8 27.5 41.3 41.8 47.3 24.1 12.2 28.1 32.8 48.7 9.4 30.2 Shi et al. [43] P + N 67.3 54.4 34.3 17.8 1.3 46.6 60.7 68.9 2.5 32.4 16.2 58.9 51.5 64.6 18.2 3.1 20.9 34.7 63.4 5.9 36.2 Cinbis et al. [9] P + N 56.6 58.3 28.4 20.7 6.8 54.9 69.1 20.8 9.2 50.5 10.2 29.0 58.0 64.9 36.7 18.7 56.5 13.2 54.9 59.4 38.8 Wang et al.[56] P + N + A 80.1 63.9 51.5 14.9 21.0 55.7 74.2 43.5 26.2 53.4 16.3 56.7 58.3 69.5 14.1 38.3 58.8 47.2 49.1 60.9 48.5 Joulin et al. .8 30.0 18.5 4.0 62.3 64.5 42.5 8.6 49.0 12.2 44.0 64.1 57.2 15.3 9.4 30.9 34.0 61.6 31.5 36.6

	[27]	P	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-24.6
	Ours	P	50.3 42

Table 7 .

 7 CorLoc and CorRet performance (%) on mixed-class PASCAL07-all. (See text for 'any'). metric aero bicy bird boa bot bus car cat cha cow dtab dog hors mbik pers plnt she sofa trai tv Av. any CorLoc 40.4 32.8 28.8 22.7 2.8 48.4 58.7 41.0 9.8 32.0 10.2 41.9 51.9 43.3 13.0 10.6 32.4 30.2 52.7 21.8 31.3 37.6 CorRet 51.1 45.3 12.7 12.1 11.4 21.2 61.9 11.6 19.2 9.7 3.9 17.2 29.6 34.0 43.7 10.2 8.1 9.9 23.7 27.3 23.2 36.6

	Evaluation

Although Eq.(1) is a valid probabilistic model, using h(x|D) as defined by Eq.(2) is a heuristic way of estimating p(x|D) in terms of "pseudo likelihood". Like all its "probabilistic Hough transform" predecessors we know of[START_REF] Barinova | On detection of multiple object instances using Hough transforms[END_REF][START_REF] Leibe | Robust object detection with interleaved categorization and segmentation[END_REF] 33], however, it lacks a proper probabilistic interpretation.

[START_REF] Ballard | Generalizing the Hough transform to detect arbitrary shapes[END_REF] Here we focus on the use of match confidence for object discovery rather than the final individual matches. For more details on PHM, see our webpage: http://www.di.ens.fr/willow/research/PHM/.

In our experiments, the use of more neighbor images does not always improve the performance while increasing computation time.

Acknowledgments. This work was supported by the ERC grants Activia, Allegro, and VideoWorld, and the Institut Universitaire de France.


Frédéric Clavert

Benoît Majerus

Nicolas Beaupré

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Limits

What is the on-line echo of the commemoration of the centenary of the 1st World War ? What is the behavior of Memorial/Heritage Institutions about the 1st World War on Twitter? How do they pass information about the Centenary ? Is there an effect of the English predominance on Twitter about the Centenary on the way non-English twitter accounts are considering the 1st World War?

How?

140dev, an Open Source PHP script (http://140dev.com). Gephi. IRaMuTeQ. Home-based server / Research project without institutional support yet.

Since the 1 st of April 2014: more than one million tweets, around 70% are retweets. Less than 50% have mentions. More than 300 000 accounts are involved. A bit less than 50 000 hashtags.

Twitter as a good field to analyze relationships between history and collective memory, memorial institutions and citizens, historians and a wide non-academic audience Social network analysis of the relations between users (mention or RT = link) Text analysis of the content of the tweet (only original tweets, without RTs).

Why Twitter? The fact that Twitter API, though sometimes very unstable, is very convenient to use is one of the criteria of this choice. But is it really pertinent in terms of research? Shouldn't we have broader sources? How to extrapolate the project's results to other online social networks? Last but not least, the difficulty to anticipate the hashtags to be collected might introduce biases in our research

Results

Future

• Studying more focused "communities": the use of #ww1 by public historians for instance • Working with other historians and sociologists whose research could benefit from the harvested data • The question of "temporalities" and their imbrications (the temporality of Twitter / the temporality of users / the temporality of the commemorations / the temporality of the First World War itself)..

Language issue. English is overwhelmingly present. Around 10% only of the collected tweets are not in English.The fact that Twitter is an English-based social network does not explain fully this disequilibrium. The Memorial institutions' communication policies on Twitter seem to explain it better.

The decentralized communication policy of British memorial institutions (t he BBC and all its Twitter accounts or the Imperial War Museum for instance) is obviously efficient

The more centralized French communication policy of the Mission du centenaire is less efficient. French WW1-related museums don't have any Twitter accounts or have one but do not follow implicit twitter rules (like the use of a general hashtag such as #ww1).

The first Marne Battle is not really present in the database, as the hashtags used by French museums were very precise and not as general as #ww1. As a consequence, they were not integrated in the general "discussion" on Twitter and few of French tweets about the Marne battle were retweeted.

British and French are not commemorating WW1 the same way . French are remembering the soldiers (Poilus). British citizens are remembering soldiers, but also battles. The French are focusing on the end of the war -the armistice, 11 th November.

The British focus on the way they entered the war.

Content of English tweets: clustering