Relevance Feedback in Content-based 3D Object Retrieval A Comparative Study

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1 753 Coputer-Aded Desgn and Applcatons 008 CAD Solutons, LLC Relevance Feedback n Content-based 3D Object Retreval A Coparatve Study Panagots Papadaks,, Ioanns Pratkaks, Theodore Trafals 3, Theohars Theohars and Stavros Perantons Natonal Center for Scentfc Research Deokrtos, {ppapadak, pratka, sper}@t.deokrtos.gr Natonal and Capodstrean Unversty of Athens, theotheo@d.uoa.gr 3 Unversty of Oklahoa, ttrafals@ou.edu ABSTRACT In ths paper, we present a coparatve study that concerns relevance feedback (RF) algorths n the context of content-based 3D object retreval. In ths study, we eploy RF algorths whch range fro query odfcaton and ultple queres to one-class support vector achnes (SVM). Furtherore, we eploy pseudo relevance feedback (PRF) and show that t can consderably prove the perforance of content-based retreval. Our coparatve study s based upon extensve experents that take nto account datasets contanng generc as well as CAD odels. Keywords: relevance feedback, pseudo relevance feedback, 3D object retreval, CAD DOI: 0.37/cadaps INTRODUCTION When developng a content-based nforaton retreval syste, the classcal approach s to focus, prarly, on the selecton of features that are best suted for characterzng the nforaton to be retreved. If the selected features are suted for nforaton retreval n agreeent to the user s crtera, then the user s satsfed and the purpose of the retreval syste s fulflled. Ths s prarly feasble n specalzed applcatons where the seantcs of the nforaton to be retreved can be captured unabguously by a set of low-level features,.e. the noton of nforaton slarty s rgd rather than subjectve. However, n the case where the seantcs of nforaton are ore coplex, the dependence of slarty estaton fro huan subjectvty ncreases whch poses sgnfcant ltatons n the perforance of a statc retreval syste. Ths s due to ts nablty to adapt to the varyng nforaton nterpretatons of dfferent users or changes n the preferences of a sngle user as well as the seantc gap proble whch ples the lack of correspondence between the low-level features used by the achne and the hgh-level, seantc nforaton used by the huan. To brdge the seantc gap and enable the achne to retreve nforaton through adaptng to ndvdual categorzaton crtera, relevance feedback was ntroduced as a ean to nvolve the user n the retreval process and gude the retreval syste towards the target. Relevance feedback was frst used to prove text retreval [9], later on successfully eployed n age retreval systes [6],[9],[0],[5],[8],[0] and lately n 3D object retreval systes [],[3],[8],[3]. It s the nforaton of relevance wth respect to a subset of the retreved results, acqured fro the user s nteracton wth the retreval syste. Generally, the RF procedure can be descrbed as follows. The syste uses a set of low-level features to present a lst of results accordng to the slarty wth respect to an ntal query. Then, the user browses through the retreval lst and provdes nforaton to the syste wth respect to the relevance of a subset of the results. Usng the relevance feedback, the retreval syste adjusts ts paraeters n order to atch optally the user s classfcaton crtera. Then, based on the adjusted paraeter settng, a new retreval sesson s ntated, new results are presented to the user and the above procedure s repeated untl the user s satsfed.

2 754 Pseudo relevance feedback, also known as local or blnd relevance feedback, s dfferent fro the conventonal approach n that the user does not provde any relevance feedback at all. Instead, the requred nforaton s obtaned based only on the unsupervsed retreval result. The procedure coprses two steps. Frst, the user subts a query to the syste whch uses the low-level features to produce a ranked lst of results whch s not dsplayed to the user. Second, the syste reconfgures tself by only usng the top closest atches of the lst, based on the assupton that they are relevant to the user s query. In ths paper, we focus on the usage of relevance feedback for the case of 3D object retreval and experent on the perforance of RF algorths whch range fro query odfcaton and ultple queres to one-class support vector achnes (SVM). Furtherore, we eploy pseudo relevance feedback and show that t can consderably enhance the retreval perforance. We present our results through an extensve evaluaton n datasets contanng generc as well as CAD odels.. RELATED WORK Most of the technques that have been proposed to explot the relevance feedback nforaton can be classfed nto the followng categores: (a) Query odfcaton - refneent reforulaton; n ths RF technque, a new query s constructed usng the feature vectors of the objects that the user consders as relevant. The new query wll be closer n feature space to the huan target thus, n the next query teraton, ore relevant objects wll be ncluded n the lst of results. (b) Feature space transforaton; the syste learns the portance of the feature space densons(assung uncorrelated features), by easurng the contrbuton of each denson n dstngushng the objects that the user consders relevant fro the rest of the objects. Then, the feature space s transfored nto a new space where the relevant objects are ore closely clustered and the successve query s perfored n the transfored space. (c) Clusterng of labeled results; the objects that are labeled as relevant by the user are consdered to for a cluster n the feature space. Addtonal clusters ay be fored n the case where the user s relevance feedback provdes varous degrees of relevance for the labeled objects or n the case were objects arked as rrelevant belong to ultple classes. Subsequently, the slarty between the query and the objects of the database s re-evaluated by consderng the relatve poston of each feature vector wth respect to the clusters. (d) Statstcal approach; the rankng of the objects n the retreval lst s deterned usng ther probablty to be relevant to the query as estated fro the user s relevance feedback. Currently, there has been very lted research n the area of relevance feedback for 3D object retreval and ost efforts were based on cobnng the aforeentoned categores. Elad et al. [8], were the frst to ncorporate relevance feedback wth 3D object retreval. Ther approach was to learn the dstance functon so as to optally separate the relevant fro the rrelevant results by an approprate argn whch was coputed by solvng a quadratc optzaton proble. In the work of Bang et al. [3], the descrptors of the unlabeled vsual objects ove toward or away fro the query, accordng to ther relatve poston n the feature space, wth respect to the relevant and rrelevant exaples. The authors call the proposed approach feature space warpng. Lefan et al. [], used Lnear Dscrnant Analyss (LDA) [7] cobned wth Based Dscrnant Analyss (BDA) [4]. In the hybrd schee, LDA was used when the user s relevance feedback was lted snce t was found to perfor better than BDA n that case, whle BDA perfored better for larger nubers of labeled results. In the work of Lou et al. [3], the syste oves the query pont to the centrod of the relevant results and re-weghts the feature densons based on ther contrbuton n dscrnatng the relevant fro the rrelevant results, by easurng the features varance. In the approach of Atosukarto et al. [], the syste learned the portance of dfferent feature representatons and pushed possble rrelevant odels to the botto of the retreval lst. The portance of each feature representaton was deterned by estatng the rato of the nuber of the relevant odels over the total nuber of odels wthn the hyper-sphere spanned by the relevant odels. The unlabeled odels that led close to labeled rrelevant odels where consdered rrelevant as well and were pushed to the botto of the lst, thus leavng the possble relevant odels close to the top of the lst. In the work of Akbar et al. [], the syste learns the portance of the ultple features of the vsual descrptor usng the varance of the dstances of the relevant objects wth respect to each other as well as the respectve rank postons for each dstnctve feature. Then, the dstances of the unlabeled odels wth respect to the query, that are

3 755 close to the rrelevant odels are set to the axu value whle n the opposte case the dstance s set to the nu dstance fro the set of the relevant odels. Novotn et al. [6] evaluate the perforance of kernel-based ethods for relevance feedback n 3D odel retreval ncludng SVM, one-class SVM [5], BDA [4] and KBDA. In ther experents, usng 3D Zernke descrptors as features, they conclude that the SVM perfored the best just above KBDA. Pseudo relevance feedback was frst eployed for text retreval [4][4] n order to autoatcally expand the query wth addtonal keywords taken fro a set of top ranked docuents whch were assued to be relevant to the query. To our knowledge, ths s the frst te that pseudo relevance feedback s used for 3D object retreval. 3. DETAILED DESCRIPTION OF METHODS Ths Secton s dedcated to the detaled descrpton of the RF ethods that we exane (Secton ), naely One-class SVM, Query Modfcaton and Multple Queres, as well as the descrpton of a pseudo-relevance schee (Secton 3.4) that s further encountered n the evaluaton stage. It s worth notng that the selecton of the RF ethods used for our coparatve study do not requre the user to select rrelevant to the target retreved objects but only relevant to the. Ths choce ay avod certan requreents whch are set when rrelevant objects are selected. These requreents coprse () selecton of large tranng datasets for rrelevant objects snce selecton of sall tranng datasets s not representatve of the whole populaton; () weghng of the objects that are labeled as rrelevant snce they belong to ore than one classes and therefore should not be equally treated. Furtherore, we should note that requrng the user to select only postve exaples becoes a less heavy task snce selecton of rrelevant objects can becoe coplcated due to a greater aount of user nteracton and ental effort that s requred. 3. One-Class SVM One-class SVM (OC-SVM) s a novelty detecton algorth [] that was frst used wthn a relevance feedback fraework by Chen et al. [5] for age retreval. The algorth s gven a set of observatons (feature vectors) that all belong to the target class and uses the to fnd a hyper-sphere n feature space that contans ost of the observatons whle havng the nu possble radus. The surface of the hyper-sphere plays the role of the decson argn, that s, patterns that le wthn the hyper-sphere are consdered to belong to the target s dstrbuton and those that le outsde the hyper-sphere are consdered rrelevant. In our case, we requre all objects of the database to be ranked based on ther slarty to the target class therefore each object s ranked accordng to ts dstance fro the hypersphere s center. In the followng we gve the detaled descrpton of the OC-SVM classfcaton algorth. Let X x, x,..., x denote a set of observatons that belong to the target class, where s the nuber of observatons and x T where N T. Let be a feature appng fro T to a dot product space H and k( x, y) a kernel functon such that ( x), ( x) k( x, x ), where.,. denotes the dot product n H. We want to copute a hyper-sphere wth center c and radus R that contans the axu nuber of observatons x whle havng the nu possble radus. Ths s forulated as an optzaton proble descrbed as follows: where ch, R, v x c nze R, subject to ( ) R and 0,,,...,. denote the slack varables whch are ntroduced to pert the excluson fro the hyper-sphere s nteror of outler observatons. If all observatons were to be explctly bounded by the hyper-sphere s surface then possble outlers would ncrease the radus of the hyper-sphere and therefore lt the generalzaton ablty of the algorth. The paraeter v (0,] s used to control the aount of slack. By decreasng the value of v, we ncrease the tolerance of the algorth to outlers reseblng a hard argn classfer, whle ncreasng the value of v we acheve the opposte effect, reseblng a soft argn classfer. (3.)

4 756 To solve the proble, we convert the objectve functon to ts dual for and derve the Lagrangan, fro whch we get the dual proble: nze jk( x, xj) k( x, x), subject to 0 and. v (3.), j Eqn. (3.) s solved through quadratc progra optzaton and gves the coeffcents center c of the hyper-sphere. The fnal decson functon s gven by: f( x) sgn R ( c ( x)) sgn R c c( x) ( x) fro whch we derve the sgn R ( ) ( ) ( ) ( ) x x x x (3.3) sgn R j ( ) ( j) ( ) ( ) ( ) ( ) x x x x x x, j sgn R jk( x, j) k(, ) k(, ) x x x x x, j If f( x) then x les nsde the hyper-sphere and therefore s consdered to belong to the target class, whle f f( x ), x les outsde the hyper-sphere and s not consdered to belong to the target class. To copute the radus of the hyper-sphere we solve the equaton f( x) 0 where x, s a support vector,.e. 0. For a support vector x, ( c ( x)) wll be equal to the radus R of the hyper-sphere snce all support vectors le on the decson surface. In our case, we rank all objects of a database accordng to ther dstance fro the hyper-sphere s center c, thus an unlabeled object of the database n ranked usng a odfed verson of f( x) whch s gven by: f ( x ) k( x, x ) k( x, x ) k( x, x ) (3.4) j j, j If f ( x ) 0, then x concdes wth the center of the hyper-sphere, thus exhbtng the hghest possble slarty wth the target class. As f ( x) ncreases, objects are consdered less slar to the target class and are ranked further fro the top of the retreval lst. 3. Query Modfcaton The query odfcaton technque s used to ove the query closer to the objects that belong to the target class, thus provng a nearest neghbor search by retrevng ore relevant objects on the top of the lst. Ths s based on the assupton that the ntal query s probably a rough representaton of what the user s searchng for and therefore when relevance feedback s suppled to the syste then a new query can be found that s closer n feature space to the user s target. In the followng, we gve the detaled descrpton of the query odfcaton algorth. Let X x, x,..., x denote a set of observatons belongng to the target class where feature vectors x correspond to the objects that are labeled as relevant fro the user, based on the retreval results that were acqured fro an ntal query x q. We copute a odfed query x as the average of xq and all x, gven by: The new query x q q x x q x q (3.5) s the centrod of the ntal query and all relevant objects and thus, t s a better representaton of the user s target. The unlabeled objects of the database are then ranked accordng to ther slarty aganst the odfed query x. q

5 Multple Queres An alternatve approach to perfor RF n content-based retreval s to perfor several queres and cobne the ndvdual results to a sngle output. Ths technque s based on the prese that a sngle query ay be nsuffcent to express the user s target and suggests usng ultple queres to search for the sae target. In the followng, we gve the detaled descrpton of the ultple queres algorth. Let X x, x,..., x denote a set of observatons belongng to the target class where feature vectors x correspond to the objects that are labeled as relevant fro the user, based on the retreval results that were acqured fro an ntal query x q. Snce the objects wth feature vectors x atch the user s target we perfor addtonal queres usng the objects arked as relevant to the ntal query. After the copleton of ths step we obtan ranked lsts of objects. We cobne the results of each ndvdual query accordng to the followng schee. Let dst( x, x ) denote the dstance between two objects O j, O k. An unlabeled object O j of the database s ranked accordng to ts overall slarty fro the set of queres, denoted as s(o j ) whch s coputed as: s(o ) dst( x, x ) (3.6) j k 3.4 Pseudo Relevance Feedback In pseudo relevance feedback (PRF), the top closest atches are consdered as relevant to the query and are consequently used as tranng data. The relevance assupton for the top closest atches s not always vald and rrelevant retreved objects that ay appear n the top of the lst wll be erroneously consdered as relevant. Ths case s known as query drft phenoenon and ples the scenaro where the retreval syste s sled by the rrelevant data and drawn away fro the user s target. However, f the low level features are dscrnatve enough to brng a large porton of the relevant objects to the top of the lst, then the relevance assupton wll be vald n the ajorty of queres thus the average perforance wll prove. Ths eans that PRF s ostly useful at ncreasng the perforance n the case of features that already exhbt good dscrnaton ablty. Otherwse, n the case where the features used are not dscrnatve enough, PRF ay actually decrease the overall perforance of retreval. The pseudo relevance feedback technque that we use coprses an off-lne and an on-lne stage. Both stages are based on the dea of ovng the feature vector of an object closer to ts cluster centrod n feature space, thus provng the perforance of a nearest neghbor search, whch s slar n sprt to the query odfcaton technque. In the followng, we gve the detaled descrpton of each stage: Off-lne stage Let n be the nuber of objects n the database and x denote the feature vector of an object O where =,,..., n. We use each object as a query n the reanng set of n objects of the database and take n ranked lsts of results denoted as lst. For each O, we take the descrptors of the top closest atches fro the correspondng lst, whch are assued to be relevant to O. Then we update the descrptor x accordng to the followng forula: x x x (3.7) whch denotes the average feature vector between the orgnal descrptor x of the object and a new feature vector rel x whch s coputed usng the correspondng top closest atches and s gven by: where x, j s the j th closest atch to object O. The x, j, where the dscountng factor j j rel j k * x, j j rel j x (3.8) j rel x feature vector s coputed by averagng the top descrptors s used to wegh the closest atches nversely proportonal to ther rank n the j k

6 758 retreval lst. Ths weghng together wth the fact that the updated descrptor s the average of the orgnal x and x rel, reduces the effect of the query drft phenoenon,.e. when the top closest atches nclude rrelevant objects as well. On-lne stage Upon subsson of a query, the syste coputes the descrptor of the query x q and copares t to the updated descrptors x of the objects of the database. Ths produces a sorted lst of results lst q fro whch the top closest atches are used to update the descrptor of the query as n Eqn. (3.7) and Eqn. (3.8). In the sequel, a new retreval sesson s ntated usng the updated descrptor of the query x, whch s closer n feature space to ts cluster-class centrod and the results are shown to the user n decreasng order of slarty. The nuber of the closest atches that are consdered as relevant to the query s deterned by the expected recall of the eployed low level features, near the top of the retreval lst. The hgher the recall the ore closest atches we expect to be relevant to the query. Eployng the above pseudo relevance feedback technque, ntroduces an addtonal cost n the te coplexty of the overall retreval procedure. In partcular, the te requred for the copleton of the retreval process s doubled. Ths s due to the executon of two retreval sessons, the frst usng the orgnal descrptor of the query and the second usng the updated descrptor of the query after eployng the pseudo relevance feedback. 4. EXPERIMENTAL RESULTS In ths secton, we present the perforance evaluaton of the relevance feedback algorths taken nto consderaton, naely, one-class SVM (OC-SVM), query odfcaton (Q-Mod) and ultple queres (Mul-Q) along wth the eployed pseudo relevance feedback schee (Pseudo-RF). As a 3D object sgnature, we use the CRSP descrptor [7] whch attans top perforance along wth low te requreents. CRSP captures the global shape of a 3D object usng a set of concentrc sphercal shells where the object s surface s projected and the resultng sphercal functons are expressed through ther sphercal haronc representaton. It s a shape sgnature that s dscrnatve enough to eploy PRF and ts low te requreents enable the applcaton of RF whch poses sgnfcant te constrants snce t s an on-lne procedure. To evaluate the perforance of the RF algorths we have used the followng datasets:. The dataset of the Prnceton Shape Benchark (PSB) that contans generc 3D odels [].. The dataset of the Watertght Models Track of SHREC 007 (WM-SHREC) [3].. The dataset of the Engneerng Shape Benchark (ESB) that contans CAD odels []. To evaluate the perforance n the PSB dataset, we have used the coarse classfcaton, whch contans 7 classes, naely, vehcle, anal, household, buldng, furnture, plant and - where the - class contans those odels that do not belong to any of the reanng 6 classes. We reoved the odels belongng to the - class and evaluated the perforance n the reanng dataset consstng of 593 odels where each class contans 66 odels on average. The ESB dataset contans 866 odels whch are classfed nto 45 classes wth 9 odels per class on average and the WM-SHREC dataset contans 400 odels whch are equally dstrbuted n 0 classes. The perforance n each dataset was easured usng the leave-one-out ethod,.e. usng each object of a dataset as a query n turn and evaluatng the perforance n the reanng set of objects. The objects that are labeled as relevant and are consequently used as tranng data are those that appear wthn the top k results and belong to the query s class, accordng to the classfcaton fle of the correspondng dataset. For the PSB dataset, we set k=40 whle for the ESB and WM-SHREC datasets we set k=0. The axu nuber of objects that are labeled as relevant wthn the frst k results s set to =0 objects per feedback teraton for the PSB dataset and =4 objects per feedback teraton for the ESB and WM-SHREC datasets. For the OC-SVM ethod we used the Gaussan kernel wth 30 and set the aount of slack to v 0.5. To depct the perforance we use the precson and recall evaluaton easures. Recall s the rato of relevant retreved odels to the total nuber of relevant odels whle precson s the rato of relevant retreved odels to the nuber of retreved odels. In Fg., we copare the perforance of all relevance feedback technques ncludng the pseudo relevance schee (Pseudo RF) for all three q

7 759 datasets. For the ethods that requre the user s nteracton (OC-SVM, Q-Mod, Mul-Q) the perforance s depcted at the 3 rd (#3) teraton of relevance feedback. Fg. : Evaluaton of OC-SVM, Q-Mod, Mul-Q and Pseudo RF for the PSB, WM-SHREC and ESB dataset. For the relevance feedback technques that requre the user s nteracton, the perforance at the 3 rd teraton (#3) s used.

8 760 The results shown n Fg. ndcate that the Mul-Q ethod has the best overall perforance. Moreover the OC-SVM ethod sees to acheve the hghest precson values for sall values of recall but ths s not consstent for hgher values of recall. Not surprsngly, the Pseudo RF ethod s ranked last whch s due to the fact that the tranng data are not provded by the user but are taken autoatcally fro the top of the retreval lst based on the assupton that they are relevant. However, the results show that Pseudo RF s an approach that can gve a consderable gan n the overall retreval perforance, whch s ore obvous n the WM-SHREC dataset where the average precson has ncreased by 5.4%. In the reanng datasets the overall ncrease n the average precson s approxately.7%. In Fg., we evaluate the perforance of each ethod for dfferent nubers of top k results fro whch the relevant objects are chosen, naely k=0, k=30 and k=40, usng the PSB dataset. The horzontal axs shows the relevance feedback teraton and the vertcal axs shows the average precson for recall n the range of (0%-50%). For the th feedback teraton (>), we use not only the new labeled objects but those objects that were labeled as relevant fro prevous teratons as well. k=0 k=30 k=40 0,7 0,7 0,7 Average precson (0%-50% Recall) 0,65 0,6 0,55 0,5 0,45 0,4 Mul-Q Q-Mod OC-SVM Intal Average precson (0%-50% Recall) 0,65 0,6 0,55 0,5 0,45 0,4 Mul-Q Q-Mod OC-SVM Intal Average precson (0%-50% Recall) 0,65 0,6 0,55 0,5 0,45 0,4 Mul-Q Q-Mod OC-SVM Intal 0, , , Relevance feedback teraton Relevance feedback teraton Relevance feedback teraton Fg. : Evaluaton of OC-SVM, Q-Mod and Mul-Q wthn the PSB dataset, for dfferent nubers of top k results (k=0, 30, 40) fro whch the relevant objects are chosen, across consecutve relevance feedback teratons usng the average precson for recall n the range of (0%-50%). Fro Fg., we can conclude that the Mul-Q and Q-Mod ethods have the best perforance and that OC-SVM s ranked last. Interestngly, the Q-Mod ethod perfors better than Mul-Q n the frst relevance feedback teraton, but t does not show sgnfcant ncrease n perforance n the followng teratons. On the contrary, the perforance of the Mul-Q ethod exhbts a greater ncrease n perforance and surpasses the Q-Mod ethod after the nd teraton. In Fg. 3, we gve soe exaple queres wthn the ESB dataset and the correspondng results frst by usng the lowlevel features and then by eployng relevance feedback usng the Mul-Q ethod. We show exaple queres fro the ESB dataset due to the fact that objects have been prarly classfed accordng to the object s functonalty whle lowlevel features are used to easure the geoetrc slarty between objects. Ths eans that objects wth very slar shapes ay belong to dfferent categores. Ths s a characterstc of the ESB dataset and n any cases t s possble to dscrnate such objects by only usng a set of low-level features and the use of relevance feedback becoes peratve n order to prove the retreval results. The exaple shown n Fg. 3(a) llustrates an ordnary query where retreval precson gradually ncreases across consecutve relevance feedback teratons, whle the exaples shown n Fg. 3(b)-(c) llustrate queres where after the nd relevance feedback teraton the retreval accuracy s excellent.

9 76 Intal Iteraton # Iteraton # Iteraton #3 (a) Intal Iteraton # Iteraton # (b) Fg. 3: Exaple queres wthn the ESB dataset wth the correspondng retreval results usng the low-level features (Intal) and the Mul-Q ethod after successve relevance feedback teratons. The query s shown n blue color, the odels that belong to the query s class are shown n green and the reanng odels are shown n red. The odels

10 76 that are labeled as relevant by the user n the current teraton are ndcated wth a blue check box, whle those that were labeled n prevous teratons are ndcated wth a gray checkbox. Intal Iteraton # Iteraton # (c) Fg. 3 (contnued). 5. CONCLUSIONS In ths paper we evaluate the perforance of a set of standard relevance feedback technques, naely, one-class SVM, query odfcaton and ultple queres for content-based 3D object retreval. The data requred to tran each ethod are obtaned fro postve feedback,.e. the user only labels a set of objects that are relevant to the target, n order to nze the user s nteracton wth the syste and antan hs/her attenton to the target class. The ultple queres ethod exhbts the best overall perforance showng a faster rate of proveent across consecutve relevance feedback teratons. In addton, we eploy a pseudo relevance feedback schee that shfts the feature vector of an object towards the centrod of ts neghborhood n feature space and show that t can add a consderable gan n the overall retreval perforance. 6. ACKNOWLEDGEMENT Ths research was supported by the Greek Secretarat of Research and Technology (PENED 3D Graphcs search and retreval 03 E 50). 7. REFERENCES [] Akbar, S.; Kung, J.; Wagner, R.; Prhatanto, A. S.: Mult-Feature Integraton wth Relevance Feedback on 3D Model Slarty Retreval, Int. Conf. on Inforaton Integraton and Web-based Applcatons Servces, 006, [] Atosukarto, I.; Kheng, W. L.; Huang, Z.: Feature Cobnaton and Relevance Feedback for 3D Model Retreval, th Int. Conf. on Multeda Modelng, 005, [3] Bang, H.; Chen, T.: Feature Space Warpng: An Approach to Relevance Feedback, Int. Conf. on Iage Processng, 00. [4] Buckley, C.; Salton, G.; Allan, J.: Snghal, A.: Autoatc Query Expanson usng SMART: TREC 3, TREC, 994. [5] Chen, Y; Zhou, X. S.; Huand, T.: One-class SVM for Learnng n Iage Retreval, Int. Conf. on Iage Processng, 00, [6] Cox, I.; Mller, M.; Mnka, T.; Yanlos, P.: An Optzed Interacton Strategy for Bayesan Relevance Feedback, Int. Conf. on Coputer Vson and Pattern Recognton, 998.

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