Deformable Part Descriptors for Finegrained Recognition and Attribute Prediction


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1 Deformable Part Descrptors for Fnegraned Recognton and Attrbute Predcton Nng Zhang Ryan Farrell,2 Forrest Iandola Trevor Darrell ICSI / UC Berkeley 2 Brgham Young Unversty 2 Abstract Recognzng objects n fnegraned domans can be extremely challengng due to the subtle dfferences between subcategores. Dscrmnatve markngs are often hghly localzed, leadng tradtonal object recognton approaches to struggle wth the large pose varaton often present n these domans. Posenormalzaton seeks to algn tranng exemplars, ether pecewse by part or globally for the whole object, effectvely factorng out dfferences n pose and n vewng angle. Pror approaches reled on computatonallyexpensve fter ensembles for part localzaton and requred extensve supervson. Ths paper proposes two posenormalzed descrptors based on computatonallyeffcent deformable part models. The frst leverages the semantcs nherent n stronglysupervsed DPM parts. The second explots weak semantc annotatons to learn crosscomponent correspondences, computng posenormalzed descrptors from the latent parts of a weaklysupervsed DPM. These representatons enable poolng across pose and vewpont, n turn factatng tasks such as fnegraned recognton and attrbute predcton. Experments conducted on the CaltechUCSD Brds 2 dataset and Berkeley Human Attrbute dataset demonstrate sgnfcant mprovements over stateofart algorthms.. Introducton Despte the many mportant applcatons and domans under nvestgaton, fnegraned recognton remans very challengng. As descrbed n [23], what often dfferentates basclevel categores s the presence or absence of parts (e.g. an elephant has 4 legs and a trunk, whereas subordnate categores are more often dscrmnated by subtle varatons n the shape, sze and/or appearance propertes of these parts (e.g. elephant speces can be dstngushed by localzed cues such as ear shape and sze. Localzng and descrbng the object s parts therefore becomes central to uncoverng ts fnegraned dentty. Several approaches have been proposed for localzng and descrbng object parts n fnegraned domans. Posenormalzaton was proposed for fnegraned recognton by Farrell et al. [23] and extended n Zhang et al. [47]. Ths paradgm seeks to dscount varatons n pose, artculaton and camera vewng angle by localzng semantc object parts and extractng appearance features wth respect to those localzed parts. In these approaches part localzaton was accomplshed usng Poselets [, ], whch requre computatonallyexpensve fter ensembles for part localzaton as well as extensve supervson. Parkh et al. [36, 37] provde an alternate way of detectng basclevel category objects such as dogs and cats by usng a Deformable Part Model [24] traned specfcally on the head. Once a head has been detected n a test mage, ths regon s used to ntalze a grabcut [38] segmentaton and obtan a mask/shouette of the entre object. Classfcaton s then performed usng a combnaton of the head shape and features extracted from wthn the head/body mask. Whe ths method s relatvely effectve for frontfacng cats and dogs, subjects n other domans (such as brds and people often exhbt greater varaton n pose and appearance and can be far more dffcult to segment from ther surroundngs (see examples n Fgure. In ths work, we ntroduce deformable part descrptors (DPD, a robust and effcent framework for posenormalzed descrpton based on DPM parts and demonstrate ts effectveness for both fnegraned recognton and attrbute predcton (see Fgure 2. We propose two deformable part descrptors: the DPDstrong leverages the semantcs nherent n stronglysupervsed DPM parts; the DPDweak explots semantc annotatons to learn crosscomponent correspondences, computng posenormalzed descrptors from weaklysupervsed DPM parts. We present stateoftheart results n evaluatng our approach on standard fnegraned and domanspecfc attrbute datasets ncludng the CaltechUCSD brds dataset [3] and Human Attrbutes dataset []. An endtoend opensource mplementaton of our method has been released wth ths paper and s avaable at
2 Partbased OnevsOne Features (POOF proposed by Berg and Belhumeur [6] whch leverages robust keypont predcton learnng a descrptor coordnate frame for each par of keyponts. Approaches such as [4, 36, 37] use regonlevel cues to estmate object segmentatons whch factate fnegraned classfcaton. Some technques use humansntheloop, askng human annotators to clck on object parts, answer questons regardng object attrbutes [3], or mark the regon that best dfferentates two confusng categores [7]. Templatebased methods have also been nvestgated by Yao et al. [45] and by Yang et al. [43]. Such approaches use a set of fxedposton templates, thereby mtgatng much of the computatonal cost of sldng wndow approaches, yet lack the spatal flexbty to deal wth substantal pose varaton Attrbute Predcton Fgure. Robustness of Deformable Part Descrptors (DPDs. Whe they work well for generally homogeneous objects such as dogs and cats, headseeded segmentatonbased descrptors often fa for other domans such as brds and humans. Examples of ths are shown n the mddle column. The ground truth segmentaton s overlad n red; the grabcut based segmentaton [38], seeded wth the head as foreground and everythng outsde the boundng box as background, s overlad n whte. The rght column shows our part localzaton results usng the stronglysupervsed DPM. Best vewed n color. 2. Background 2.. Fnegraned categorzaton Fnegraned classfcaton has recently emerged as a topc of great nterest. A growng body of lterature has proposed varous technques and has addressed recognton on a large number of fnegraned domans. These domans nclude: dog breed classfcaton [26, 29, 37], subordnate categores of flowers [2,, 33, 34] and plants [4, 39], recognton of nvertebrates [32], fnegraned classfcaton on subsets of ImageNet [6] such as fung [5], and speceslevel categorzaton of brds [23, 46]. We antcpate more work n the near future on manmade categores such as cars [4] and arplanes [3]. Followng recent work by Belhumeur et al. [5] on localzng fducal ponts n faces, Lu et al. [29] present an alternate approach to bud an exemplarbased geometrc and appearance models for detectng dog faces and localzng the facal keyponts. Another work n ths ven s that of Sfar et al. [39] who classfy leaves and flowers by descrbng appearance wth multple coordnate or vantage frames. A very recent and closely related contrbuton s the Attrbutebased representatons [22, 27, 28] present another promsng drecton, offerng the possbty of recognzng a novel category wth only a category descrpton (no magery. Relevant subsequent work on attrbutes ncludes that of Parkh and Grauman [35] explorng relatve attrbute strength and that of Berg et al. [7] and Duan et al. [9] propose automatc dscover of attrbutes to ad n fnegraned classfcaton. Perhaps the most closely related work on attrbute predcton s Bourdev et al. [], whch uses features extracted from Poselet [, 9] actvatons to predct nne bnary attrbutes on mages of humans. Another promsng alternatve to Poseletbased approaches for effectve transfer of pose annotaton from tranng mages to test mages s the Exemplar SVM proposed by Malsewcz et al. [3] Deformable Parts Model Inspred by the Pctoral Structures work of Fschler and Elschlager [25], the Deformable Parts Model (DPM of Felzenszwalb et al. [24] has become one of the most effectve and wdelyused object detecton approaches to date. The object s represented by a coarse root HOG fter and several hgher resoluton part fters. The DPM model uses a mxture of components to capture varaton n vewpont and/or pose (e.g. for a car, the three components mght correspond roughly to the front, sde and threequarter vews. Stronglysupervsed DPM Whe the lmted supervson requred for the DPM s advantageous, the latent parts provde no semantc nformaton about the object whch makes posenormalzaton challengng. Related work has used strong supervson to tran DPMs for human pose estmaton [4, 44], part localzaton [2] and object detecton [3]. Whe these methods focus on detecton, our objectve s dfferent, poolng semantc part features across components to derve a posenormalzed representaton.
3 Test Image Part Localzaton Posenormalzaton Stronglysupervsed DPM Classfcaton Class CN... Class C2 Class C Predcted Class: Lncoln Sparrow Ψpn Weaklysupervsed DPM Attrb αn... Attrb α2 Attrb α P(α[sMale] =.2 P(α2[hasHat] = P(αN[hasShorts] =.38 Fgure 2. Overvew. Both of the proposed Deformable Part Descrptors (DPDs are depcted above. The frst descrptor (top row apples a stronglysupervsed DPM [3] for part localzaton and then performs posenormalzaton by poolng features from these nherently semantc parts. The posenormalzed features Ψpn n Equaton 3 can be used for ether fnegraned recognton or attrbute predcton (as ndcated by the dotted arrows. The second descrptor employs a weaklysupervsed DPM [24] for part localzaton and then uses a learned semantc correspondence model to pool features from the componentspecfc localzed latent parts nto semantc regons (the posenormalzed descrptor Ψpn shared by all components. In the part to regon poolng (the black, yellow, magenta, and cyan lnes, wder lnes ndcate hgher weght. Best vewed n color. In ths work, we adopt a varant of the stronglysupervsed DPM [3]. We use object part annotatons and am to localze the semantc parts for posenormalzed descrptors. Instead of ntalzng the fxedsze part fters heurstcally (energy maxmzaton, we ntalze the part fters by usng semantc part annotatons. As n [3], we ntalze the mxture components by clusterng the annotated poses nstead of usng the aspect rato. After tranng the mxture of root fters, we process the tranng mages to get the component assgnment for each tranng example. Then, for each component c, the part fters of ths component are ntalzed at the average relatve locatons wth average sze among all the examples wth component assgnment c. Unlke [3], we do not mpose constrants on part overlap durng tranng. Notaton For both types of DPM, we use a latent SVM to tran the model parameters. The matchng score of a model β for a gven mage I s n X Spart (p, I, β + b S(I, β = Fβ Φ(I, p + = Spart (p, I, β = Fβ Φ(I, p d Φd (dx, dy ( where Fβ s the root fter, Fβ s the part fter, b s the bas term, Φ(I, p s the mage feature vector of the subwndow located at poston p, (dx, dy and d are respectvely s the dsplacement and dsplacement weght of the th part, and Φd (dx, dy = (dx, dy, dx2, dy 2 s used to penalze the part dsplacement[24]. The overall score s computed usng the best possble placements,.e. fβ = maxz Z(I S(I, β where Z(I s the set of all possble latent values. The model s traned by teratvely assgnng the best possble placements and learnng the parameter β by stochastc gradent descent. Then, the part locatons are predcted by Z(I = argmax(z,β S(I, β s.t. O(p (I, bbox > δ Z(I = (c(i, p (I,, pn (I (2 where c(i s the component assgnment, p (I s the predcted boundng box, p (I s the predcted locaton of part p and O(p, bbox s the overlap between the predcted boundng box and ground truth boundng box. δ s a threshold to control predctons and we set t to be.65 n our experments. Gven the part localzatons, we show below how to form DPMbased posenormalzed descrptors and pool them across components, n a manner analogous to what the Posepoolng Kernel (PPK [47] does va Poselets. 3. Deformable Part Descrptors (DPD We use the deformable part model (DPM s demonstrated effectveness for detectng objects as a foundaton for our posenormalzed approach to fnegraned recognton and attrbute predcton. We do ths as a faster and more relable alternatve to Poselets whch were prevously proposed for posenormalzaton n both fnegraned recognton [23, 47] and attrbute predcton []. As noted earler,
4 we are not the frst to consder usng DPMs for fnegraned recognton. Parkh et al. [36, 37] traned a DPM model to locate a cat s or a dog s head and then used ths detecton both to descrbe the head appearance and to seed a segmentaton whch would recover the rest of the body (See Fgure. Our goal s to use DPM to localze the parts and pool the posenormalzed mage features nduced by the part locatons. 3.. Stronglysupervsed DPD The underlyng prncple n posenormalzaton s that one can decompose an object s appearance as observed n one mage and compare t to the same object (or object category as observed n a dfferent mage. Ths decomposton generally equates to localzng semantc parts and then descrbng them. From the DPM models, suppose that we have a total of C components ncludng mrror pars C = {c (, c (2,..., c (C }, where for odd values of j, c (j+ s the mrror component of c (j. Each component c (j has a set of parts P (j = {p (j, p(j 2,..., p(j P }, p(j denotng the th part for component c (j. We now defne a posenormalzed representaton wth R semantc poolng regons R = {r, r 2,..., r R }. We defne the posenormalzed representaton as Ψ pn (I = [Ψ (I, r, Ψ (I, r,..., Ψ (I, r R ]. (3 where I, r l s the pooled mage feature for semantc regon r l and I, r s the mage feature nsde the root fter or boundng box and we concatenate the mage features together to get the fnal posenormalzed representaton Ψ pn. To derve the posenormalzed representaton for a gven detecton Z(I n Equaton 2 (from component c (j = c(i, we must fgure out a mappng S (j : P (j R. For each part p (j, we seek to determne whch posenormalzed poolng regon or regons {r l } the features I, p (j should be mapped nto. For stronglysupervsed DPM, we use the semantc part annotatons and t s straghtforward to pool the correspondng part descrptor across dfferent components by settng the semantc regons the same as the semantc parts from the strong DPM,.e. R = P (j for all j {,..., C}. I, r l = I, p (j l j {,..., C} ( Weaklysupervsed DPD Usng a weak DPM, the latent parts of dfferent components have no explct semantc correlaton, nor s such correspondence guaranteed to exst. We have explored two optons for dealng wth ths: one based on a percomponent appearance representaton wth no semantc parts; the other leverages addtonal annotatons to estmate semantc correspondence of the latent parts across components. The percomponent representaton s very straghtforward. The tranng and test sets are parttoned nto subsets accordng to whch component fres on them. A separate classfcaton model s traned for each component usng the features extracted on that component s subset of tranng mages. Ths creates reasonable classfers, however, there are two shortcomngs. Frst, the parts carry no semantc nformaton (though ths s not nherently necessary. Second, the tranng data s fragmented across the C components, so models wl be accordngly weaker. Tranng fragmentaton s partcularly problematc for fnegraned recognton where you may only have 53 tranng examples total for each category. Experments usng ths model are ncluded n Secton 4.3. The second representaton, however, solves both of these problems. By provdng semantc annotatons at tranng tme, we can model semantc correspondence between the latent parts of dfferent components. In effect, we learn the semantc dentty of each latent part n the model and can thus pool features to a global posenormalzed model, ndependent of whch component fres durng detecton. We model the posenormalzaton as a weghted bpartte graph G = (P, R, W where w (j W ndcates the, the th part of component c (j con degree to whch p (j trbutes to r l. Ψ (I, r l = N P = w (j ( Ψ I, p (j where N = P w(j s used for normalzaton. W s a three dmensonal matrx wth sze P R C and w (j s modeled as a functon of the annotatons A. Annotatons a k A could be keyponts (as used to tran Poselets, rectangular regons (as used to tran Strong DPM or any other type of semantc labels. In our example, we use the keypont annotatons ncluded wth the CUB2 dataset [42] and H3D dataset []. More precsely, we defne w (j as: w (j = k= A ( ρ kl overlap a k, p (j where ρ kl [, ] ndcates a specfed semantc relevance for annotaton a k to regon r l (e.g.( left ear s relevant to head, left knee s not. The overlap a k, p (j functon encodes the dstrbuton of annotaton a k wthn part p (j. Let I jk be the set of tranng mages that have semantc annotaton a k and where c (j s the component whch fres. We can formally defne the fractonal overlap ( overlap a k, p (j = {I I jk a k (I p (j }. (7 I jk It s worth notng that the tranng set need not be exhaustvely labeled wth semantc annotatons. Fewer annotatons (5 (6
5 just mean smaller I jk and thus coarser predctons of the weghts w (j Classfcaton Gven the posenormalzed representaton Ψ pn (I n Equaton 3, we employ a lnear SVM for the fnal classfcaton. For the stronglysupervsed DPD, we use the annotated semantc parts to get Ψ pn for tranng and part localzaton from Equaton 2 for testng; for the weaklysupervsed DPD, we utze Ψ pn from Equaton 2 for both tranng and testng. Due to the sparsty of tranng examples for certan poses, there wl be some test mages for whch a correct detecton cannot be found even gven a specfed object boundng box. Ths means that we don t have predctons for the part locatons n Equaton 2. In such cases, we can use the classfer traned on the feature descrptor nsde the boundng box I, r wthout any posenormalzaton. 4. Experments In ths secton, we wl present a comparatve performance evaluaton of our proposed method. We conduct experments on the commonly used fnegraned benchmark CaltechUCSD brd dataset [42] as well as the Berkeley Human Attrbute dataset from []. Our system can process several mages per second, leveragng the avaable fast DPM mplementaton n [2]. An opensource verson of our endtoend DPD mplementaton s avaable at In contrast, the fastest avaable mplementaton of Poseletbased [47] reles on a C++ remplementaton of [] and takes approxmately seconds per frame on a comparable machne. 4.. Implementaton Detas Image Features and Classfcaton After predctng the part regons va DPM, we use kernel descrptors [8] to extract feature vectors for fnal classfcaton. Specfcally, we use four types of kernel descrptors: gradent, local bnary pattern (lbp, rgb color, and normalzed rgb color. Followng the settng n [8], we compute kernel descrptors on local mage patches of sze 6 x 6 over a dense regular grd of step sze 8 and then apply a spatal pyramd on top. We use vector quantzaton of these descrptors wth a element codebook, concatenatng perregon descrptor hstograms nto a sngle vector per mage. These vectors are provded as nput to a lnear support vector machne (lblnear wth power scaled features. Effcent Weak Poolng In Equaton 6, whe a fully optmal formulaton would lkely learn the best convex combnaton of ρ kl and utze sophstcated dstrbutons for all For results usng deep convolutonal features, see [8] for more detas. pars (a k, p (j, we make two smplfyng assumptons for effcency and smplcty. Frst, we defne ρ kl as an ndcator such that ρ kl {,, } and l ρ kl = ; n other words, each semantc part a k s assocated wth one or more regons r l (n a few cases t s shared between two, such as the shoulder between head and torso regons. Ths represents parttonng the semantc parts A amongst the poolng regons R. Second, to evaluate the overlap(a k, p (j functon n Equaton 7, we evaluate the weaklysupervsed detector on the semantcally annotated data, notng for each detecton where the varous parts {a k } fall. Across the annotated data we accumulate dstrbutons for each part p (j (we only nclude contrbutons toward p (j when c (j fres. Whe the optmal method would lkely model these spatal dstrbutons wth respect to p (j, we relax ths and smply record the fracton of semantcally annotated mages whch have a k, for whch c (j fres and where a k has hgh overlap wth (or falls wthn p (j CaltechUCSD Brds2 Dataset We conduct our experments on the 2category Caltech UCSD brd dataset, one of the most wdely used and compettve fnegraned classfcaton benchmarks. Followng [23], we use two semantc parts for the brd dataset: head and body. We utze both the earler (CUB22 and current (CUB22 versons of the brd dataset for better comparson. CUB22 The ntal verson of the CUB2 dataset contans 633 mages of 2 dfferent brd speces n North Amerca. There are around 3 mages per class. We use the provded trantest splt whch uses 5 mages per class for tranng and the balance for testng. Each mage n the dataset has a boundng box annotaton but no other annotaton s provded. In order to tran the strong DPM wth semantc parts, we manually annotate the part locatons for the tranng mages,.e. head and body boxes. If one of the parts s not vsble, we mark ts vsbty state as. Gven those annotatons on tranng mages, we tran a strong DPM wth 5 components, each wth these two parts. Due to the lack of addtonal annotatons, we are unable to evaluate the DPDweak approach on ths dataset. Table shows the mean accuracy of our method on the CUB22 dataset. We also compare wth other publshed methods, ncludng MKL [3], random forest [46], TrCos [4], template matchng [43], segmentaton [] and the recentlypublshed Bubblebank method [7]. One baselne s to use the same mage descrptors used by our methods nsde the boundng box but wthout any pose normalzaton,.e. KDES [8], whch yelds 26.4% mean accuracy, better than some prevous methods. Our approach acheves 34.5% mean accuracy, whch outperforms the best prevous
6 Method MKL [3] Random Forest [46] KDES [8] TrCos [4] Template matchng [43] Segmentaton [] Bubblebank [7] DPDstrong2 Mean Accuracy(% %.7% 32.7% 2.5% 3.3% 3.% 2.6% HEAD 7.5% 2.7% 25.5% 2.8% 5.4%.% 3.9% 4.% BODY 2 Table. Results on CUB22 dataset. We note that [7] uses addtonal human labels. The best performance s acheved by the twopart stronglysupervsed DPD model (DPDstrong2. Method KDES [8] Template matchng[43] Oracle DPDweak8 DPDstrong2 DPDstrong2nohead DPDstrong2nobody Mean Accuracy(% Table 2. Results on CUB22 dataset usng KDES features. The best performance s acheved by the 8part weaklysupervsed DPD model (DPDweak8. Oracle s akn to DPDstrong but uses the ground truth head and body locatons. result [7], and that requres human annotatons obtaned usng a crowdsourced onlne game to fnd the most dscrmnatve regons. CUB22 The CUB22 s the latest verson of the dataset, whch ncludes more hghqualty mages and has 5 part annotatons, e.g. beak, crest, throat, lefteye, leftwng, nape, etc. Ths dataset contans,788 mages of the same 2 brd speces as CUB22. We use the default tranng/test splt, whch gves us around 3 tranng examples per class. We tran both a weak DPM and a strong DPM to factate part localzaton. For the strong DPM, we tran a mxture of fve components, each wth two parts and we partton the keypont annotatons to generate the head and body part annotatons. Specfcally, we choose the semantc part annotatons as the mnmum rectangular regon to cover the part s specfed subset of keyponts. Ths strong DPM enables part localzaton wth the followng accuraces: the head part wth 45.% precson and 43.5% recall, the body part wth 77.5% precson and 75.2% recall. Here we mark a correct predcton when the predcted part box and the ground truth part box have an overlap over. For the weak DPM, we tran a mxture of three components, each wth 8 parts and usng poolng as descrbed n Equaton 5. The poolng weghts learned for the brd model are HEAD %.% 9.6%.2%.%.%.% TORSO 2.4% 2.6%.2% 2.9% 27.2%.3% 26.3%.2% LEGS.%.2% 32.%.2%.6% 8.7% 8.2% 29.9% Fgure 3. Learned Percomponent Poolng Weghts. Pctured are examples of the weghtng models that are learned for the weaklysupervsed brd (above and human (below models (DPDweak. Shown s just one of the components for each model. In each weght matrx, rows correspond to the semantc regons that the eght latent parts (columns are pooled to. We emphasze that the weghts are learned automatcally. Best vewed n color. vsualzed n the top part of Fgure 3. Table 2 presents our classfcaton results on the CUB22 dataset usng KDES [8] features. To compute the baselne method, we use only the boundng box regon, yeldng 42.53% mean accuracy. We also evaluate the template matchng method of [43], usng code released by the authors. Our DPDstrong acheves 5.5% mean accuracy and DPDweak acheves 5.98% on ths dataset, outperformng other stateofart methods. To understand the contrbuton of ndvdual DPM parts to the classfcaton accuracy, we blnd a DPDstrong to ndvdual semantc parts n an ablaton study. These results are ncluded n Table 2. We fnd that localzng the head part of the brd s especally mportant: removng the head part, the CUB22 accuracy falls to 43.5%. Addtonal experments on the mportance of ndvdual parts are avaable on our DPD project webpage. In terms of other publshed work on ths dataset, PPK [47] makes use of Poselet actvatons to generate posenormalzed representaton and acheves a
7 Attrbute Freq SPM [] Poselets [] Percomponent DPDweak8 DPDstrong3 s male has long har has glasses has hat has tshrt has long sleeves has shorts has jeans has long pants Mean AP Table 3. Results on the Human Attrbutes dataset. Freq s the label frequency (fracton of specfed attrbutes that are postve and Percomponent gves the results when usng the percomponent poolng method dscussed n the begnnng of Secton 3.2. The values above denote mean average precson. For the full PrecsonRecall curves, see Fgure 4. Another baselne usng the same mage features but consderng only the boundng box regon acheves 66.58% mean AP. mean accuracy of 28.8%. The authors of POOF [6] recently reported performance of 56.89% enabled both by accurate part (keypont localzaton and through the tranng of thousands of classfers. We have expermented wth replacng the KDES features wth deep convolutonal features and have reported 64.96% performance (please see [8] for detas Human Attrbutes Dataset The human attrbutes dataset [] contans 835 mages collected from the H3D [] and PASCAL VOC 2 [2] datasets. There are nne attrbutes (e.g. has tshrt, has jeans, and each one has a label of {,, } respectvely meanng absent, present and unspecfed. We use the H3D data to tran a strong DPM wth 3 components and 3 semantc parts (head, torso and legs and a weak DPM wth 3 components and 8 parts. Example poolng weghts learned for the weak DPM are vsualzed n the lower part of Fgure 3. Smar to the CUB22 dataset, the semantc part annotatons are generated from the keypont annotatons avaable from H3D. We then test on both the tranng and test mages of the human attrbutes dataset to localze the parts and get posenormalzed mage descrptors for attrbute predcton. Table 3 shows predcton results on these nne attrbutes. Two baselnes are ncluded for comparson: SPM whch uses spatal pyramd match nsde the boundng box and the Poselet approach of Bourdev, et al.; both results are taken from []. We also nclude the results of usng percomponent classfers, measurng average precson under the precsonrecall curve. PrecsonRecall curves for each of the nne attrbutes are shown n Fgure 4. We use mean AP nstead of mean accuracy for ths dataset because the percentage of postve examples for each attrbute s qute vared. From the table, we see that both DPD methods outperform posenormalzaton usng Poselets and moreover, our method s approxmately 3 tmes more effcent. AP: 73.4 AP: 7.3 AP: 6. s male AP: 83.7 AP: 82.9 AP: 82.4 has hat has shorts AP: 64. AP: 59.4 AP: 45.5 has long har AP: 72.5 AP: 7. AP: 67.8 has tshrt has jeans AP: 5.2 AP: 49.8 AP: 46. AP: 78. AP: 77. AP: 54.7 AP: 78. AP: 76.5 AP: 74.2 has glasses AP: 55.6 AP: 4.7 AP: 38. has long sleeves has long pants AP: 93.5 AP: 93. AP: 9.3 Fgure 4. Attrbute Predcton PrecsonRecall Curves. Results are shown for each gven attrbute. The blue curve and mean average precson (AP scores are for the DPDstrong. The green curve and mean AP scores are for the DPDweak. The red curve and mean AP scores are for the prevous startofthe art technque of Bourdev et al. []. The dashed lne ndcates the frequency (fracton of postves. Best vewed n color. 5. Concluson In ths paper we have proposed Deformable Part Descrptors (DPDs, a posenormalzed representaton based on DPMs. We descrbed two such posenormalzed methods, respectvely applcable to stronglysupervsed and weaklysupervsed varants of deformable part models. The frst method explots the semantcs nherent n the stronglysupervsed DPM s parts, poolng them drectly to form a posenormalzed descrptor. The second uses semantc an
8 notatons to learn crosscomponent correspondences between parts of the weaklysupervsed DPM. These correspondences are then used to generate a posenormalzed descrptor. We have evaluated the proposed DPD methods, surpassng the prevous stateoftheart performance on standard datasets for both fnegraned recognton and attrbute predcton. In concluson, we outlne some drectons for future work. Frst, we suggest that a greater number of supervsed parts (as used n Azzpour et al. [3] would ncrease the descrptve power of the DPD model. However, to do ths, we would need to address the ssue of selfoccluson. Second, learnng of crosscomponent part correspondences could be enhanced by consderng unconstraned convex combnatons for the semantc relevance coeffcents ρ kl and more optmal overlap( functons that address the spatal dstrbutons of the semantc annotatons wthn parts, nstead of smply consderng occurrence frequency. Acknowledgments Ths research s funded by NSF grants IIS64 and IIS22928, by DARPA s Mnds Eye and MSEE programs, and by the Toyota Motor Corporaton. The thrd author s supported by the NDSEG Fellowshp program. References [] A. Angelova and S. Zhu. Effcent object detecton and segmentaton for fnegraned recognton. In CVPR, 23. [2] A. Angelova, S. Zhu, and Y. Ln. Image segmentaton for largescale subcategory flower recognton. In WACV, 23. [3] H. Azzpour and I. Laptev. Object Detecton Usng StronglySupervsed Deformable Part Models. In ECCV, 22. [4] P. N. Belhumeur, D. Chen, S. Fener, D. Jacobs, W. J. Kress, H. Lng, I. Lopez, R. Ramamoorth, S. Sheorey, S. Whte, and L. Zhang. Searchng the Worlds Herbara: a System for Vsual Identfcaton of Plant Speces. In ECCV, 28. [5] P. N. Belhumeur, D. W. Jacobs, D. J. Kregman, and N. Kumar. Localzng Parts of Faces Usng a Consensus of Exemplars. In CVPR, 2. [6] T. Berg and P. N. Belhumeur. 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