Combining Appearance Models and Markov Random Fields for Category Level Object Segmentation

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1 Combnng Appearance Models and Markov Random Felds for Category Level Object Segmentaton Dane Larlus LEAR - INRIA, INPG Fre de rc Jure LEAR - INRIA, Caen Unversty dane.larlus@nralpes.fr frederc.jure@uncaen.fr Abstract Object models based on bag-of-words representatons can acheve state-of-the-art performance for mage classfcaton and object localzaton tasks. However, as they consder objects as loose collectons of local patches they fal to accurately locate object boundares and are not able to produce accurate object segmentaton. On the other hand, Markov Random Feld models used for mage segmentaton focus on object boundares but can hardly use the global constrants necessary to deal wth object categores whose appearance may vary sgnfcantly. In ths paper we combne the advantages of both approaches. Frst, a mechansm based on local regons allows object detecton usng vsual word occurrences and produces a rough mage segmentaton. Then, a MRF component gves clean boundares and enforces label consstency, guded by local mage cues (color, texture and edge cues) and by long-dstance dependences. Gbbs samplng s used to nfer the model. The proposed method successfully segments object categores wth hghly varyng appearances n the presence of cluttered backgrounds and large vew pont changes. We show that t outperforms publshed results on the Pascal VOC 2007 dataset. Fgure 1. Instances of object categores are localzed n mages (col 1), producng masks (col 2) that can be used to automatcally extract objects (col 3). The proposed method automatcally produces these segmentaton masks wthout any user nteracton. jects belongng to known categores (also called fgureground segmentaton), assumng that the categores are defned by sets of tranng mages used to learn object appearance models1. These tranng mages play a fundamental role because object models buld from these mages allow to recognze and segment object. Fgure 1 gves an llustraton of the problem we are addressng as well as results of our algorthm. Startng from cluttered mages ncludng objects of nterest, the method s able to localze objects and to automatcally produce seg- 1. Introducton Ths paper nvestgates the problem of producng accurate and clean segmentaton of object classes n mages, wthout gvng any pror nformaton on object denttes, orentatons, postons and scales. Image segmentaton has been addressed for several decades. Many dfferent approaches have been nvestgated, tryng to combne varous mage propertes such as color, texture, edges, moton, etc., n an unsupervsed way. However, segmentaton usng only bottom-up processes usually fal to capture hgh level nformaton; mage segmentaton s ndeed deeply related to mage understandng. The problem addressed here s the segmentaton of ob- 1 Please note the dfference between mage segmentaton and object segmentaton. Image segmentaton corresponds to the stuaton where everythng n the mage have to be segmented whereas n object segmentaton, only objects of nterest are consdered /08/$ IEEE

2 mentaton masks that can be used to extract objects wthout any human nteracton. In the rest of the paper, we frst present related work and an overvew of the proposed method. Then we descrbe our model and ts estmaton. Fnally we gve expermental results and conclusons. 2. Related works It has recently been shown [4] that models that consder mages as loose sets of vsual words, as well as beng very effcent for mage classfcaton, can also be successfully appled to the localzaton of object class nstances n mages. These models robustness to orentaton and scale change allows them to cope wth large varatons n object appearance. Ths knd of model can also be combned wth Drchlet processes, to produce spatally localzed clusters [15], [11]. Unfortunately, object shapes are very badly defned by blobs, so the localzaton provded s very rough. Cao et al. [2] tred to overcome ths lmtaton by combnng segmented regons and keyponts. Regons gve a good ntal segmentaton of objects; however there s a dffcult trade off concernng ther sze. MRFs and ther varants (CRF [14] [18], DRF[13]) have a long hstory n mage segmentaton. One of the major advantages of MRFs s regularzaton. Class labels (object or background) of two neghborng pxels are correlated, and when local evdence for a label s weak, labels from the neghborhood can provde a valuable help. Shotton et al. [14] propose usng a CRF to learn a model of object classes for semantc mage segmentaton. Ther model combnes appearance, shape and context nformaton. More recently, Wn and Shotton [20] used an enhanced CRF based on a spatal orderng of object parts to handle occlusons and Verbeek and Trggs [17] proposed to combne a MRF and aspect models. Smultaneously, remarkable object segmentaton algorthms based on MRFs have been recently proposed by several authors (e.g. [12]), assumng that the object poston s roughly provded by a user n the mage to be segmented. The key dea s to model mage foreground and background color dstrbutons; these dstrbutons are teratvely estmated by graph-cut. These nteractve algorthms obtan very good results and the next step s now to get rd of user nteractons. We would lke to segment objects n a large amount of mages only by specfyng the object category (e.g. segment the cows ). However segmentatons obtaned by MRFs wthout shape models rarely produce accurate segmentaton, thus several authors tred to merge these two concepts. One of the most notceable work s the one of Kumar et al. [5] who propose a methodology for combnng CRFs and pctoral structure models. Lebe and Schele [6] use hand segmented mages to learn segmentaton masks correspondng to vsual Fgure 2. Images from the Pascal VOC 2007 dataset for two dfferent categores: brds (frst lne) and sofa (second lne). codebook entres; then the Implct Shape Model allows to localze objects and segment mages. Several other very nterestng papers [1, 13, 19] or more recently [7] propose dfferent ways to combne shape models wth segmentaton. However, the smple geometrc assumptons made by models usng shapes do not allow to deal wth complex appearances of weakly structured object classes (see Fgure 2). At the end, only a very few of these methods can produce accurate segmentaton of objects havng wde appearance ranges. As can be seen on the last Pascal VOC challenge results (detaled secton 4), there remans much room for mprovement, especally when backgrounds are too rch and cluttered to be explctly modeled. Overvew of our approach. The man contrbuton of ths paper s a tractable model sutable for object segmentaton, that takes advantage of two complementary components: (a) a model wth MRF propertes for ts ablty to produce felds of locally coherent labels and to produce segmentaton fttng wth low level mage boundares, (b) a bag-of-words based object model, allowng the recognton and the localzaton of objects despte strong vew pont varatons, and ensurng long range consstency of vsual nformaton. 3. Model descrpton Each mage s seen as a regular grd of patches. Patches are descrbed by a generatve model made of several regons (large sets of patches) whch can take any label of nterest (one of the object classes or background) and are estmated by the algorthm for each mage. The segmentaton conssts n assgnng patches to regons. The number of regons and ther postons are unknown. At the same tme, ths grd of patches s seen as a feld of labels where we apply regularzaton between neghborng patches Vsual Features Two dfferent types of nformaton are extracted from any mage to be segmented: a set of n overlappng patches

3 Fgure 3. The model computes the best assgnment of patches to object blobs and algn cuts wth natural boundares of mages. and a gradent map. Overlappng vsual patches. Patches, denoted P, {1,..., n}, are square mage regons. Four dfferent characterstcs are computed from each patch. Frst of all, a vsual codebook s obtaned by clusterng SIFT [8] representatons of the patches. Then, each patch P s assocated to the closest codeword. The assgned codeword s denoted ; ths s the frst characterstc. We also produce vsual words based on color nformaton by clusterng color descrptors [16]. The patch P s also characterzed by ts closest color codebook word w color. A RGB value s computed by averagng over pxels extracted n the center of the patch. Ths 3D-vector s denoted rgb. Fnally we also consder the coordnates of the patch center X = (x, y ) n the mage. Gradent Map. In addton to computng patch-based characterstcs, we also extract a gradent map G, that gves the strength of the gradent at each pxel locaton (x, y). The map s computed by the algorthm of [9], that responds to characterstc changes n several local cues assocated wth natural boundares. Thus, the nformaton carred by an mage s entrely summarzed by the gradent map G and the w sft characterstcs of the n overlappng patches P,, w color, rgb, X, 1 n}. {w sft 3.2. The blob-based generatve model.e. Ths secton specfes a generatve model sutable for rough object/background segmentaton. We use a model nspred by [15] wth explct spatal structure nformaton: we consder that an mage s made of regons of ellptc shape that we call blobs, and that each blob generates some patches wth ts own model. Intutvely, f an mage contans three objects (a car, a pedestran and a bke), we may have three blobs, one over each object regon. Each blob s then responsble for generatng the mage pxels n ts regon, by generatng a set of patches whch appearance corresponds to the object category (car patches for the car blob, and so on). We also have a background regon generatng the remanng patches. Ths enforces the spatal coherence of the generated patches over the blob regon. Probablty of the set of patches P gven the Drchlet model s obtaned by multplyng probabltes of each patch P gven the model. The generaton of a patch requres to a) select a regon (object blob or background) and b) generate a patch usng the patch model specfc to that regon. The remanng of ths secton detals the probabltes of selectng a regon, and of generatng a patch gven a regon. The blob generaton s assumed to follow a Drchlet process. The Drchlet process exhbts a self-renforcng property; the more often a gven value has been sampled n the past, the more lkely t s to be sampled agan. Drchlet processes can be seen as the lmt as K goes to nfnty of a fnte mxture model usng K components 2 [10]. It means that for each new generated patch, t can ether belong to an N k n 1+α already generated mage blob B k, wth probablty where N k s ts populaton, or ether start a new regon wth α a probablty n 1+α, α beng the concentraton parameter of the Drchlet process. These probabltes wll be called p dr n the next secton. We characterze each blob B k, 1 k K, wth a set of random varables: Θ k = {µ k, Σ k, C k, l k, N k }. µ k, Σ k are the mean and the covarance matrx descrbng the ellptc shape of the blob, l k s the blob label (object category), C k s a Gaussan mxture model representng the colors of the blob, N k s the number of patches generated by the blob. The background s defned by a color dstrbuton C b g. We characterze each patch P by ts features, w color, rgb, X ) and also by two other random varables b and c. b s the ndex of the regon (object blob or background) that generates the patch (1 b K) and c s the color mxture component the patch s assgned to (detaled later). Let us defne the probablty of generatng a patch P, gven that t s generated by the regon B k of parameters (w sft Θ k (whch means that b = k). It s made of 4 dstnct parts, as the model assumes that patch poston, color and appearance are ndependent gven the blob they belong to. p(p Θ k )=p(w sft =p(w sft, w color, rgb, X Θ k ) Θ k )p(w color Θ k )p(rgb Θ k )p(x Θ k ) The poston X of a patch follows a unform dstrbuton for the background and a normal dstrbuton of parameters µ k and Σ k for object blobs : p(x Θ k, l k ) = N (X, µ k, Σ k ). We assume that object blobs and background have a color model made of a Gaussan Mxture (wth 3 components n our experments), as suggested by [12]. Ths allows to capture object-nstance and mage-background specfc color appearances and buld regons of coherent appearance even f some parts are less nformatve. Ths s 2 K could go to the nfnty whle n practce the fnte number of patches makes K fnte and bounded (1)

4 dfferent from the color word w color whch encodes classspecfc color appearance. For smplcty, we assume that each patch s generated by a unque GMM component, and ths s encoded n the varable c ntroduced earler. Fnally, the probabltes of the SIFT and color codewords only depend on the class label,.e. p(w sft Θ k )=p(w sft l k ) and p(w color Θ k )=p(w color l k ). These dstrbutons encode object appearance nformaton and are responsble for the recognton ablty of our model. Ths s the only nformaton shared between mages. They are learned va annotated tranng mages from whch vsual words are extracted. The dstrbutons are estmated by a countng process; ndeed we count how often each vsual word appears n each class and how often t appears n the background A MRF structured feld of blob assgnment. The assgnment of patches to object blobs or background b = {b, 1 n} determnes the segmentaton of the mage. That segmentaton s enhanced wth our second component, the MRF of blob assgnment, whch regularzes the assgnment of neghbor patches and also algns cuts wth natural mage contrast. Ths feld s defned on a grd (8- connectvty) that corresponds to patch centers. It s defned over labels whch are the b values prevously ntroduced.e. the regons assgnment. In the prevous secton, the generatve model fully defnes the probablty p(p b, Θ) of generatng all patches gven the assgnment b and the blob parameters Θ. Then pror probablty of the label feld does not depend on the Θ parameters and s assumed to combne two ndependent models p(b) p dr (b)p mrf (b). The frst part p dr comes from the Drchlet process descrpton of the blobs dstrbuton. and the second part p mrf encodes neghbor dependences mposed by the MRF. Our model consders the jont probablty of patch observatons and blob assgnment, whch s decomposed n p(p, b Θ) p(p b, Θ)p(b Θ) p(p b, Θ)p dr (b)p mrf (b) where P represents observatons assocated to patches, b the label feld and Θ all parameters related to the generatve model. Ths jont probablty can be rewrtten usng an energy functon E, p(b, P) exp( E), whch makes the formulaton of the MRF easer: (2) E = U + γ,j N V,j (3) where N represents couples of graph neghbors n the patch grd, γ s a constant parameter whch weghts the proporton of the two terms and U = log(p(p b, Θ)p dr (b)) (4) The sum over V,j represents the model p mrf. It s defned as V,j = [l b l bj ] exp( βφ,j ), (5) where [.] s the ndcator functon. V,j s a potental that enforces local coherence of the object/background labels, va constrants on the smlarty of neghbor patch labels, and also encourages cut along the mage gradent va the functon Φ. Φ,j s the maxmum gradent G value between the poston of the center of the patches P and P j, β a constant computed as n [12] (see Fg.3 for an llustraton). Thus, V,j s null f patches have smlar labels, else t partcularly penalzes patches that have dfferent labels and no boundary n between, as we want to allow our model to separate objects and background manly at mage boundares. From ths energy-based formulaton we can go back to the exponental form and derve the posteror probablty for the blob assgnment labels, that we wll need later for the model estmaton. To do so, we consder only the observaton P matchng ste b and the clques of the graph contanng b. N b p(b b, Θ, P) p(p Θ b ) n 1+α exp( γ V,j ) where b denotes b \ {b } Model Estmaton,j N The model beng defned by the blob component and the MRF structure, ts parameters have to be estmated for each mage to produce object blobs labels {l, 1 K} and patches assgnments to blobs {b, 1 N}. A Gbbs sampler generates an nstance of parameter values from the dstrbuton of each varable n turn, condtonal on the current values of the other varables. Ths secton defnes the condtonal dstrbutons on each varable and the way to sample them. The set of parameters to be estmated s {µ 1:K, Σ 1:K, C 1:K, l 1:K, b 1:n, c 1:n }. Samplng blob parameters. In the followng, observatons from {P, 1 n} belongng the blob B k are renamed as P = (w sft, w color, rgb, X ), 1 N k. The two frst blob parameters are µ k and Σ k. If W 2 denotes a Wshart dstrbuton, µ k N (µ, Mean(X 1:N 1 K ), N k Cov(X 1:N K )) Σ k W 2 (Cov(X 1:N K ), N k 1) The thrd blob parameter s the color mxture of Gaussans, whch s smply estmated by a stochastc EM, wth each mxture made of nc (3 n our experments) components. We also estmate the color mxture for the background. (6) (7)

5 Recall Bke Graz w sft +w color +rgb+x Precson w sft +w color +X w sft +w color +rgb w sft +rgb+x w color +rgb+x Fgure 4. The relatve mportance of the dfferent features : SIFT (w sft ) and color codebooks (w color ), color components (rgb) and postons (X) dfferently combned. Note that the MRF component comes wth the X varable. The last blob parameter s the class label l k, sampled from: l k N k =1 p(w sft l k )p(w color l k ) (8) due to the assumpton of patch ndependence gven the blob that has generated them. Samplng patch parameters. c s the color mxture component affected to a patch. It s computed by samplng from the assocated regon color Gaussan mxture. Last but not least, the condtonal estmaton of the blob membershp varables b. The probablty of generatng b condtonally on the other varables and the observatons p(b b, Θ, {b }, P) was ntroduced equaton (6). Tranng data. The probabltes p(w sft k l k ) and p(wk color l k ) are essental to the object model. We drectly learned them from tranng mages. If segmentaton masks are avalable they can be used advantageously. We wll see n the experments that only havng the boundng boxes s enough to obtan accurate segmentaton From labeled patches to pxels Our model provdes a probablty of blob assgnment for each patch, and a probablty of class label (one of the object class) for each blob. From those, we can compute the class label probablty for a patch. The probablty for pxel p to belong to an object or to the background s computed by accumulatng the knowledge about all patches contanng ths pxel. Ths s modeled by a mxture model where weghts are functons of the dstance between the pxel and the center of the patch. Segmentaton masks are obtaned by assgnng the most probable class to each pxel. 4. Experments We consder manly three challengng datasets for object/background segmentaton: TU Graz-02 3, Pascal VOC 2006 and Pascal VOC 2007 [3]. They contan object classes wth hghly varyng appearance together wth a generc and cluttered background. Furthermore, the objects present scale and llumnaton varatons, vewpont changes and occlusons. The TU Graz-02 mages contan three object categores : bcycles, cars, persons. Avalable ground-truth for segmentaton makes ths database nterestng to evaluate the performances of a segmentaton method and to study parameters. The Pascal VOC 2006 mages nclude strongly varyng vews of 10 dfferent categores: bcycles, buses, cats, cars, cows, dogs, horses, motorbkes, people and sheep. The Pascal VOC 2007 possesses 10 addtonal classes: brds, boats, bottles, chars, planes, potted plants, sofa, tables, trans and tv/montors. Segmentng mages contanng smultaneously many objects of such a large number of categores wth the same algorthm and the same parameter settngs s a partcularly dffcult task. Fgure 2 gves a good llustraton of these dffcultes by showng several representatve mages for 2 dfferent categores (brds, sofa). Ths secton covers three dfferent aspects of the experments that valdate our method. Frst we make a quanttatve study on Graz-02 dataset emphaszng the mportance of each feature that appears n the model. Second, we show qualtatve results (.e. segmentaton masks) obtaned on the Pascal VOC-2006 dataset. Thrd, we evaluate our method on the Pascal VOC-2007 dataset, and demonstrate that we largely outperform the best compettve methods on ths benchmark dataset. We also present addtonal experments on a related problem usng the Mcrosoft dataset Parametrc study Several features are computed from local patches: SIFT codebook ndexes w sft, color codebook ndexes w color, RGB colors rgb and postons X (see secton 3.1 for detals). Ths secton evaluates the relatve mportance of these features n the segmentaton results. We compared the full model (denoted w sft + w color + rgb + X), wth dfferent subsets of these features. We consder the Graz-02 dataset, because t comes wth a ground truth. We use half of the 300 Graz02 mages for learnng, and the remanng for testng. Vsual vocabulares of 5000 elements are created for the SIFT [8] descrptors, and 100 elements for the color [16] descrptors. They are obtaned by quantzng descrptors extracted from the tranng mages. Graz02 mages contan only one object category per mage so the segmentaton task can be seen as a bnary classfcaton problem. Thus the accuracy s measured by precson 3 pnz/data/

6 recall curves (see Fg. 4) that show how many pxels from the object categores (all mages merged) are correctly classfed. The dfferent features w sft, w color, rgb and X can be present or not n the models. We observe that the two vsual vocabulares w sft, w color are essental. If one of them s mssng the performance decreases much, but texture (.e. w sft ) s more crtcal than color. The MRF, by regularzng the segmentaton, mproves the results very much, as the comparson of the red (all features) and blue (wthout X) curves shows. That regularzaton has strong vsual effects on the precson of the segmentaton. And last, the color component rgb gves an mprovement for two categores out of three. However, when objects are not localzed correctly, the color component deterorates the results Qualtatve results In ths secton, we propose a vsual nspecton of the segmentaton masks computed on Graz02, MSRC and Pascal VOC-2006 database. For each class, mages are segmented nto object of nterest and background regons. For the Graz (bke, car and person) and MSRC, the object model s traned usng provded segmentaton masks. On the Pascal dataset (the other categores), object models are traned wth boundng boxes only, and not wth pxel level segmentaton masks, as the ground truth of the pascal challenge only provdes boundng boxes. Ths s why we cannot provde quanttatve results on ths dataset. Typcal segmentaton results are shown Fgure 1 and 4. Our algorthm automatcally detects and segments objects accurately despte large ntra-class varatons and scale and orentaton changes, even n the case of weak supervson (tranng wth boundng boxes only) Quanttatve results Due to ts popularty we compared our method wth results recently publsh on the MSRC2 dataset 4. The task s sgnfcantly dfferent because the background s dvded nto several classes (grass, buldng, trees..) so the goal s not really to do fgure/ground segmentaton but to segment mages. Table 1 gves the performance of our algorthm on the 13 object classes of the dataset. We compared wth Textonboost results [14] and wth Markov Feld Aspect Models (MFAM) [17]. Our method gves comparable results, although t s not desgned explctly for ths knd of task. Pascal challenge VOC 2007 The Pascal VOC 2007 challenge [3] s an nternatonal benchmark that nvolves tens among the best computer vson groups. Thus we use ths dataset to compare the performances of our object category segmentaton algorthm to state-of-the-art algorthms. The competton conssts n generatng pxel-wse segmentaton 4 avalable at Table 1. Results on the MSRC2 dataset gvng the class of the object vsble at each pxel, or background otherwse, whch s exactly the task we are tacklng wth our approach. The dataset s made of 20 object classes and one background class. The dataset ncludes more than 5000 mages for tranng, 422 of them are precsely annotated wth segmentaton masks. For the other mages, only the boundng boxes are gven. We evaluate our accuracy wth the Pascal VOC 2007 protocol. We compute the average segmentaton accuracy across the twenty classes and the background class. The segmentaton accuracy for a class s the number of correctly labeled pxels of that class, dvded by the total number of pxels of that class n the ground truth labelng [3]. For tranng category appearance models, we use all the annotatons (both the segmentaton masks and the boundng boxes). Then the model s estmated for each mage usng the detector INRIA PlusClass [3] to ntalze the blob postons and labels. The results obtaned on the 20 classes are presented n Table 2. We also report n ths table the best results obtaned durng the competton. Our average performance s almost 10% hgher than ths best known performance. 5. Conclusons We have presented a novel framework for object category mage segmentaton. The key element that dstngushed ths method from exstng approaches s the combnaton, wthn the same model, of two complementary components. Frst, a blob-based component detects objects usng occurrences of vsual words. It produces an approxmate segmentaton, roughly splttng the dfferent components of the mage. Second, a MRF-based component produces clean cuts, guded by mage ntensty, contour and texture edges. A Gbbs samplng algorthm allows the effcent estmaton of the parameters of the model. We have shown that our model acheves very accurate segmentaton masks on the Pascal VOC 2006 and Graz02 datasets. On most classes our method mproves on the best scores obtaned on the benchmark dataset Pascal VOC Acknowledgments The authors thank Erc Nowak for hs help and Hed Harzallah for provdng the detecton results.

7 TKK Our Method TKK Our Method backgrd table plane dog bcycle horse brd motorbke boat person bottle plant bus sheep car sofa cat tran char montor cow mean Table 2. Results on the pascal VOC 2007 dataset. Frst row gves the best results known on ths dataset (detals can be found n [3]). The second row s the result we obtan. Fgure 5. Examples of segmentaton obtaned by our method for the Graz-02, Pascal VOC 2006 and Mcrosoft (best vewed n color) datasets. For the latest the followng codng s used: G for grass, Sh for sheep, S for sky, B for buldng, T for tree, C for car. References [1] E. Borensten and J. Malk. Shape guded object segmentaton. In CVPR, pages , [2] L. Cao and L. Fe-Fe. Spatally coherent latent topc model for concurrent object segmentaton and classfcaton. In ICCV, [3] M. Everngham, L. Van Gool, C. K. I. Wllams, J. Wnn, and A. Zsserman. The PASCAL Vsual Object Classes Challenge [4] R. Fergus, L. Fe-Fe, P. Perona, and A. Zsserman. Learnng object categores from google s mage search. In ICCV, [5] M. P. Kumar, P. H. S. Torr, and A. Zsserman. OBJ CUT. In CVPR, [6] B. Lebe and B. Schele. Interleaved object categorzaton and segmentaton. In BMVC, [7] Z. Ln, L. S. Davs, D. Doermann, and D. DeMenthon. Herarchcal part-template matchng for human detecton and segmentaton. In ICCV, [8] D. Lowe. Dstnctve mage features from scale-nvarant keyponts. Int. J. Comput. Vson, 60(2), [9] D. Martn, C. Fowlkes, and J. Malk. Learnng to detect natural mage boundares usng local brghtness, color, and texture cues. IEEE PAMI, 26(5): , [10] R. M. Neal. Markov chan samplng methods for drchlet process mxture models. Techncal Report 9815, Dept. of Statstcs, Unversty of Toronto, Sep [11] P. Orbanz and J. M. Buhmann. Smooth mage segmentaton by nonparametrc bayesan nference. In ECCV, [12] C. Rother, V. Kolmogorov, and A. Blake. grabcut : nteractve foreground extracton usng terated graph cuts. ACM Trans. Graph., 23(3): , [13] J. Shotton, A. Blake, and R. Cpolla. Contour-based learnng for object detecton. In ICCV, pages I: , [14] J. Shotton, J. Wnn, C. Rother, and A. Crmns. Textonboost: Jont appearance, shape and context modelng for mult-class object recognton and segmentaton. In ECCV, pages I: 1 15, [15] E. Sudderth, A. Torralba, W. Freeman, and A. Wllsky. Descrbng vsual scenes usng transformed drchlet processes. In NIPS, [16] J. van de Wejer and C. Cordela Schmd. Colorng local feature extracton. In ECCV, pages , [17] J. Verbeek and B. Trggs. Regon classfcaton wth markov feld aspect models. In CVPR, [18] J. Verbeek and B. Trggs. Scene segmentaton wth crfs learned from partally labeled mages. In NIPS, [19] J. Wnn and N. Jojc. Locus: Learnng object classes wth unsupervsed segmentaton. In ICCV, [20] J. Wnn and J. Shotton. The layout consstent random feld for recognzng and segmentng partally occluded objects. In CVPR, pages 37 44, 2006.