Semantic Illustration Retrieval for Very Large Data Set

Transcription

1 Semantc Illustraton Retreval for Very Large Data Set Song Ka, Huang Te-Jun, Tan Yong-Hong Dgtal Meda Lab, Insttute of Computng Technology, Chnese Academy of Scences Beng, 00080, R Chna Insttute for Dgtal Meda, ekng Unversty Beng, 0087, R Chna Abstract In ths paper, we present a retreval system that performs the llustraton retreval on very large data set The tradtonal text-based retreval systems often perform poorly on the llustraton retreval, because some llustratons are uncaptoned Even worse, textual nformaton are often mxed wth nosy nformaton and therefore fal to represent the llustratons accurately To overcome the problem, we propose a semantc model for llustraton retreval In ths model, we frst extract the shape nformaton whose smlartes are then used to construct two lnk graphs Based on the graphs, we execute the auto-captonng procedure on the uncaptoned llustratons In addton, cross-modal analyss s appled to get rd of the nosy nformaton and reduce the dmensonalty of the feature vectors Fnally, we ntroduce a re-rank scheme that returns as many subtopcs related to the query as possble along wth the mprovement n relevance Experments on approxmately 500,000 llustratons showed that our system performs effcently n retrevng the llustratons wth hgh relevance and dversty Index Terms llustraton retreval, feature extracton, cross-modal analyss, rankng I INTRODUCTION As the dgtal lbrary develops rapdly, a large number of llustratons n dgtal books are avalable to the users Journalsts and publshers need to query proper llustratons for newspapers, books and artcles Hstorans and archaeologsts may need llustratons n ancent books to verfy ther fndngs Therefore, t s very mportant to develop technques for users to fnd the pctoral nformaton they want from dgtal lbrares In [3], Cohen and Gubas proposed an llustraton retreval system based on vsual features Shape nformaton s extracted for ndexng and retreval Nevertheless, the method s manly focuses on the computer-generated techncal llustratons whch are manly composed of graphcs prmtves (lnes or crcular arcs, marks, etc) Therefore, the method can not be generalzed n other complcated llustratons such as mages of people, anmals and landscapes revous work n [3] manly focuses on vsual features of llustratons and therefore fals to explore the semantcs of llustratons accurately Lbrarans usually rely on text-based retreval usng ther bblographcal catalogue as relable metadata, but manual attachment of textual metadata to the llustratons are too tme-consumng In [6][7], Hu et al proposed several methods to extract ttles and other textual nformaton from varous documents automatcally However, ttles or captons of llustratons are often mssng and some of the collateral texts are rrelevant to the content of the llustratons and therefore carry lots of nosy nformaton To overcome the problem, a method to structure the vsual content of dgtal lbrares usng Content-Based Image Retreval (CBIR) s proposed n [] By groupng the smlar mages to clusters n content, t s applcable to attach addtonal textual metadata to mages n the same clusters Ths approach s helpful f there are no resources avalable to provde suffcent textual nformaton Nonetheless, Due to the ntrnsc drawbacks of CBIR systems, ths method can not explore the underlyng semantcs expressvely Besdes, all the related works mentoned above are only for a small dataset Thus, the results are not qute persuasve and further research works are needed In ths paper, we present a system, Illustrator, attemptng to accomplsh the llustraton retreval effectvely and effcently n a large llustraton dataset In our system, a semantc model s exploted to analyze the semantcs of llustraton In ths model, we frst extract the shape nformaton whose smlartes are then used to construct two lnk graphs based on whch we execute the auto-captonng procedure on the uncaptoned llustratons In addton, cross-modal correlaton analyss s appled to get rd of the nosy nformaton and reduce the dmensonalty of the feature vectors Fnally, we ntroduce a re-rank scheme that returns as many subtopcs related to the query as possble along wth the mprovement n relevance Experments shows that our system performs effcently on llustraton retreval and the llustratons n our experments are all from dgtal books n the Chna- Amercan Dgtal Academc Lbrary (CADAL) proect To the best of our knowledge, Illustrator s the frst system for retreval n a very large llustraton dataset

2 The paper s organzed as follows Secton presents the archtecture and basc technques of text-based llustraton retreval systems, whch s also used as the baselne n our experments We present our semantcbased system, Illustrator n Secton 3 In Secton 4, experments and results are presented Fnally, we conclude our paper Secton 5 II TEXT-BASED RETRIEVAL The dea of text-based retreval s that exactng the textual features as the man descrpton to the llustratons A typcal archtecture of text-based retreval system s shown n Fgure Illustraton Set Textual Extracton Index of the llustratons, f there s any, often appear n these felds Therefore, words n these felds should be assgned to hgh weghts Besdes, the felds of the ttle along wth dfferent fonts lke bold, talcs also contan rch nformaton of the llustratons In addton, words around the llustratons, namely collateral texts, also play an mportant role n ndcatng the contents of the llustratons Apparently, words appearng at dfferent postons should be assgned wth dfferent weghts due to ther mportance to the llustratons We take the assumpton that only 0 words before or after the llustraton can help explanng the contents of llustratons The weghts assgned to the collateral words ncreases as the dstances to the llustratons decrease B Query After extractng the textual features (eg captons, ttles, collateral words), ndexes are establshed based on the extracted textual features for further queres Gven a keyword by the user, the system executes the retreval on the ndexes and fnds the relevant textual nformaton, whch s then used for mappng the correspondng llustratons The whole procedure s shown n Fgure keyword Query rocessng Illustratons Retreval Results Rankng Retreval Results Textual Features Fg Framework of text-based Retreval system Textual features are extracted from an Illustraton Set (IS) and then ndexed Gven a keyword by the user, the system executes the query on the ndex of the IS and returns the retreval results by a certan rankng scheme A Textual Feature Extracton All the dgtal books are subect to the OEB standard and pages are stored as HTML fles Snce we only concerned wth the llustratons n the books, pages wthout llustratons are elmnated n advance From the HTML fle, we extract the context n whch an llustraton appears It s conspcuous that texts n dfferent felds and postons have varous effects on the llustratons Thus, dfferent weghts should be assgned to the words accordng to ther mportance to the llustratons To begn wth, llustratons are stored n JG format, so the felds of the mg tag are sgnfcantly mportant n descrbng the contents of the llustratons The captons keyword Fg The query procedure When the dataset grows really large, the query procedure may be qute tme-consumng Therefore, the algorthm for query should be effcent and easy to scale to large dataset C Rankng Feature Index In the query procedure, we have obtaned the retreval results whch have varous relatons to the keywords How to present the llustratons wth the hghest relevance to the users becomes a problem We apply a rankng scheme n whch each of the llustratons n the retreval set s assgned to a certan score accordng to ther relevance to the query As dscussed n secton, words n the felds of mg, wth the font lke bold, talc as well as the collateral words around the llustratons contan semantc

3 descrpton of the llustratons The rankng scheme s based the factors In [], Marco La Casca et al proposed a scheme to assgn dfferent score to the texts around the llustratons Words appear n the felds of mg and n the font of bold, talc are assgned 50 and 40 respectvely Weghts of the words around the llustratons depend on ther dstance of the llustratons Takng the assumpton that only 0 words before or after the llustratons count, we 0* pos / 0 can computer the score as 50 * e, where pos s the poston of word wth respect to the llustraton After combnng all the scores together, we obtan the total score whch can be consdered to evaluate the relevance of the llustraton to the query Therefore, after rankng the scores, we can present the most relevant results to the users III SEMANTIC-BASED RETRIEVAL The tradtonal text-based retreval we dscussed above has ts own drawbacks due to the lmtatons of the textual descrpton of the llustratons Therefore, vsual features of the llustratons are exacted and a cross-modal analyss s appled to mprove the semantc representaton of the llustratons, namely semantcbased retreval The archtecture of the retreval scheme s shown n Fgure3 keyword Illustraton Set Textual Extracton Index Query rocessng Retreval Set R Re-rank Scheme Fnal Result R Book cluster Expandng Set R Fg 3 Framework of semantc-based retreval system Vsual Featur Semantc Compresson Gven a keyword, we frst apply the text-based retreval engne to obtan a set n whch the llustratons have textual relevance to the keyword We denote the set as R (see Fgure3) Then accordng to the smlartes of the vsual features, we group the llustratons nto clusters whch are used to expand the set we obtaned After applyng some semantc compresson technques and a mult-rank scheme, we receve the ultmate retreve results A Textual Feature Extracton We dscussed the textual feature extracton n secton Nevertheless, the textual features are nadequate to descrbe the llustratons Hence we extract the vsual feature and explot the underlyng relatons between the two knds of features so that we can better represent the llustratons There are manly two forms of llustratons n the dgtal books We denote the set I as the llustraton subset ncludng charts, fgures, tables, etc, whch are manly composed of graphcs prmtves (lnes, marks, crcular arcs, etc) Among the numerous modaltes (color, texture, shape, etc) of pctoral data that can help ndexng the llustratons, we focus the shape nformaton for the ndexng task The other set I ncludes the pctures of people, anmals, landscapes, etc, whch are manly gray-scale mages nstead of the colored ones Due to the dfferent features of the two knds of llustratons, we apply two methods to represent ther vsual features In [3], Cohen and Gubas proposed a method to compute the shape nformaton for ndexng Based on the assumpton that every llustraton can be represented by the basc shapes through translaton, rotaton and scalng, they start wth a collecton of basc shapes S = { S, S, S 3 S k } The elements n the set S are ether bult-n or user-defned types For every llustraton, the ndex ι () of s a subset of the set S, that s, a subsequence of S = { S S, S }, 3 s extracted to match well nto the llustraton For each matchng basc shape S, four parameters correspondng to the matches They are x and s y (the translaton), S k S nto are recorded θ (the rotaton) and log (logarthm of the scale) We denote T ( x, y, θ, s ) as the matchng transformaton of S nto The mages of people, anmals, landscapes, etc are n varous and complcated shapes, therefore, shape features we dscussed n secton 3 can not represent ther vsual features properly The most wdely used vsual features nclude: () color features such as color hstogram, color moment, color coherence vector; () texture features such as edge hstogram, co-occurrence

4 matrx and Gabor wavelet features; (3) shape feature such Fourer descrptor and moment nvarant We use moment nvarants to measure shape nformaton of the llustratons We compute a seven dmensonal feature vector of seven moments and each moment s nvarant to translaton, rotaton and scalng Although the features we extracted can not perfectly represent the orgnal llustratons, they could provde correlatons n vsual features among llustratons and therefore supply us an alternate method to dg the underlyng semantc correlatons among llustratons B Retreval Set Expanson The llustratons retreved n R are textual-relevant to the keyword However, the text features are nsuffcent to express the semantcs of the llustratons Therefore, we explot the smlartes of vsual features to expand the set R In order to expand the set, we construct two graphs wth the names of BSSF graph and MIF graph based on the basc shape features and moment nvarant features respectvely Fgure 4 depcts the man dea of the set expanson BSSF graph MIF graph Fg4 Retreval set expanson To construct the BSSF graph, gven two llustratons, Q I, Let ι () and ι() denote ndexes p of and Q, T = { T, T T } and q TQ{{ TQ, TQ,, TQ }} denote the matchng transformaton of and Q, where T ( x, y, θ, s ) ( p) denotes the matchng transformaton of S nto, TQ ( xq, yq, θ Q, sq ) ( q) denotes the matchng transformaton of S Q nto Q, we use the Hausdorff dstance to measure ther smlarty sm T, T ) = H ( T, T ) = max( h( T, T ), h( T, T )) () ( Q Q Q Q R d(, ) s the dstance between two ponts n vector space A lnk from to Q wth weght sm (, Q) s constructed f sm (, Q) ε ( ε s a threshold); otherwse no lnk n constructed What s worth beng noted s that the BSSF graph can only be appled among llustraton belongng n I For the llustratons n I, we construct the MIF graph Each llustraton can be represented as a vector v We smply calculate the smlarty between v and v as v v sm( v, v ) = cos( v, v ) = (3) v v Agan, a threshold δ s set and we note that the MIF graph can only be appled among llustratons n I Thus, each lnk n the two graphs has been assgned a weght to ndcate the smlarty between the correspondng llustraton par Usually, llustratons expressng the same meanng are smlar n ther vsual features to each other Hence, n both graphs, a group of heavly lnked documents potentally represent a semantc group, llustratons connected by weak or no lnks contan dfferent semantcs Based on the two graphs, we expand R and obtan R C Cross-Modal Correlaton (CMC) Analyss We exploted the textual nformaton to ad the retreval of llustratons However, there are approxmately one thrd llustratons are uncaptoned or the textual features are usually companed wth some nosy and rrelevant nformaton Our task s to accomplsh the auto-captonng, whch s defned as follows: Auto-Capton Gven a set of llustratons, each s wth a vsual descrpton by a 7-D vector Some of them are captoned by several keywords, whch some of them lack the textual nformaton Fnd the best words that descrbe the uncaptoned llustratons In [9], an et al proposed a method that based on the mage segmentaton and random walk technques However, the method can not be appled drectly to our problem owng to the dffcultes to segment the llustratons To solve the problem, we assume that llustratons wth smlar vsual features probably have potental textual smlartes and our deas are shown n Fgure 5 Where h( T, T ) = sup nf d( α, β ) () Q α T β T Q

5 Vsual Feature Space Fg5 Auto-captonng As llustrated n Fgure 5, v, v, v3, v4, v5 are vsual features of 5 llustratons The crcles composed by sold lnes t, t 3, t5 are textual features correspondng to v, v3, v5, whle there are no correspondng textual features to v, v4 To solve the problem, we generate the vrtual textual features (the crcles composed by dashes) by analyzng the lnkages among vsual features as well as the correlatons between vsual features and textual features We obtan the vsual feature lnkage formatons n the lnk graph (BSSF graph or MIF graph) In Fgure 5, v, v5 have vsual lnks wth v, therefore, we can obtan the textual nformaton t through t and t 5 In the same way, we receve t 4 through t3 and t 5 In general, gven an uncaptoned llustraton wth the vsual feature v, the correspondng textual features t s calculated as t = ω t (4) v D Where D = { v ( v, v ) E}, E s the edge set of the lnk graph, w s the weght between v and v The vsual and textual features can be combned nto hgh-dmensonal vectors and then be drectly used for retreval Let n stand for the dmensons of vsual features, and m stand for the dmensons for textual features, and then n the ont vsual-textual features space, each llustraton can be represented as f c = [ f = [ v V, f T ] v v v v 3 v 4 v 5 t Textual t t 3 t 4 t 5 Feature Space v, t t t,,, n,, k, m ] (5) Note that snce varous vsual and textual features can have qute dfferent varatons, we also need to normalze each feature n the ont space accordng to ts maxmum elements (or certan other statstcal measurements) However, the dmensonalty of the vsual or textual feature vectors s pretty hgh For reducng the feature dmensonalty, an often-used method s the so-called latent semantc ndexng (LSI) technque [5], whch reles on sngular value decomposton (SVD) of the feature matrx to capture the latent semantc structure among the matrx elements Thus we can apply the LSI technque to reveal the latent semantc structure n the ont vsual-textual feature space, as n [] However, LSI does not dstngush features from dfferent modaltes n the ont space, thus the optmal soluton based on overall dstrbuton may not best represent semantc relatonshps between features of dfferent modaltes In [8], two cross-modal assocaton analyss methods, e, cross-modal factor analyss (CFA) and canoncal correlaton analyss (CCA), were ntroduced to dentfy and measure ntrnsc assocatons between vsual and audo features Here we adopt the CFA method to capture the best coupled patterns between vsual and textual features The key dea underlyng the CFA method s to fnd two orthogonal transformaton matrces so that the coupled data n the two subsets of features can be proected as close to each other as possble [8] Let F v and F T be the vsual and textual feature matrces for the llustratons n R, then the transformaton matrces A and B can be obtaned by solvng the followng optmzaton: ' ' mn F A = F B s t A A = I, B B I (6) V T F = Where denotes Frobenus norm Accordng to the F orthogonalty of A and B, we have ( F F ) + tr( F F ) tr( F AB F ) F V A FT B = tr F V V T T V T (7) Where ( ) tr denotes the matrx trace Thus Eq(6) s ~ ~ T equvalent to maxmze the term tr( VAB T ) It can be shown [8] that such matrces are gven by the SVD decomposton of F F V T, e, F F V T = ADB, where D s the sngular value matrx Thus wth the optmal transformaton matrces A and B, FV and FT can be transformed by the followng equaton: ~ FV = FV A ~ (8) FT = FT B F ~ V and F ~ T can then be combned nto a ont feature matrx F ~ C Smlarly to those n LSI, the frst and most mportant k vectors n F ~ V and F ~ T can be used to preserve the prncpal coupled patterns n much lower dmensons A sgnfcant advantage of the CFA method s n favor of coupled patterns wth hgh varatons However, the CFA method s based on some naïve technques such as the lnear correlaton model and the proected dstance, whch would lmt ts applcatons n more complex stuatons Moreover, such an approach can only be appled n the cases where the feature matrx for all the testng llustratons s constructed offlne Instead, n ths paper two optmal transformaton matrces A and B are learned from the tranng llustratons n I (B), and then used as two semantc matrces to map the testng llustratons n I (T) nto another semantc space That s, for a target llustraton I (T ), the transformed feature c ~ V ~ T vector can be represented as f = [ f, f ], where

6 ~ V V ~ T T f = f A and f = f B Ths s smlar to the semantc smoothng method used n [4][0] Wthout rsk of confuson, ths paper refers to ths verson of the CFA method as cross-modal correlaton (CMC) analyss Thus f we consder the vsual-textual ont space, the C C correspondng kernel s gven by K C = [ k( f, f )], C C where k ( f, f ) s a kernel functon Here we refer to K as the content kernel C D Re-rank Method Most current retreval systems ntend to provde the retreval results to users accordng to the relevance scores of each result to the query The dea s qute eloquent when the users are clear about what they need and provde the exact key to the system However, n most occasons, the users fal to present the accurate keywords and therefore ther ntentons are expressed ambguously It s a good dea that the system returns as many subtopcs of the query topc as possble wthout the loss of relevance In ths case, users can pck up the llustratons they are nterested n and therefore the possblty that users get satsfactory results enhances sgnfcantly However, n tradtonal retreval research, precson and recall are two factors to evaluate the performance of retreval system But both of the two crtera only concern the relevance of the results, wthout consderng the varety of the topcs the results cover Therefore, the top search results are often bounded nto a group of closely related topcs and fal to meet the needs of the users dversfed requrements To overcome such stuaton, we ntroduce a re-rank scheme based on a penalty algorthm Gven the lnk graphs we constructed n secton 3, the scores we computed n secton 3 and the expanded retreval set R, the penalty algorthm are executed as follows: Step 0 Intalze the score s of each llustraton n set R, the fnal result set R = Φ Step Sort the llustratons n R by ther scores n descendng order Step Suppose the llustraton ranked hghest n R s Add nto R and delete n R, then fnd all the llustratons that have a lnk to n the lnk graphs and mpose a penalty to the score of each as follows: c c s = s sm( f, f ) s Step3 Update the scores and re-sort the llustratons n R n descendng order Step4 Go to Step untl B = Φ Note that the vtal part of the above algorthm s Step, whch contans the penalty dea that reduce the scores of llustratons whch have semantc lnk wth the chosen llustratons n the fnal result set By executng the penalty algorthm, we always keep the most nformatve llustratons n the top of R and therefore provde a varous subtopcs n the fnal result set IV EXERIMENTS We conducted experments to demonstrate the retreval result of the Illustrator system outperform that of the tradtonally text-based retreval systems All the llustratons along wth the textual pages used n our experments are obtaned n dgtal books The dataset contans approxmately 500,000 llustratons, we fltered the llustratons whose wdth and heght are both smaller than 00 pxels, and whose rato between wdth and heght are greater than 5 or less than /5 A Relevance The recall (R) and precson () are often used to evaluate the relevance of the results to the query Due to the huge number of llustratons n the dataset, t s qute dffcult to label all the results n advance Hence only the precson s consdered as the crteron In addton, we only concerned the top 0 results We pcked up 0 keywords and executed the query Fgure 6 shows that Illustrator sgnfcantly mproves the value of precson compared to the text-based retreval (TBR) systems Fg 6 recson of Illustrator and text-based retreval system B Dversty The factor dversty s used to evaluate the number of topcs related to the query n the returned llustratons Agan, we pcked up 0 keywords and executed the query and only the top 0 results are taken nto consderaton Fgure 7 shows that the Illustrator system provdes much more topcs than the text-based retreval (TBR) systems

7 E A Case Study We provde a case study to show how our system works Fgure 7 shows the nterface for Illustrator We nput the word forest as the query keyword and the top 5 results are shown n Fgure 8 Fg 6 Dversty of Illustrator and text-based retreval system C Dmenson Reducton We also performed experments to evaluate the effects of the LSI and CMC analyss on our dataset When llustratons are represented by textual features, we can explot LSI analyss to reduce the feature dmensonalty; when llustratons are represented by both vsual and textual features, both LSI and CMC can be used Table shows that average dmenson reducton ratos (DR) We can see that both LSI and CMC can effectvely reduce the dmensonalty of the feature space Comparatvely, the CMC analyss has large DR Note that here we use the same egengap based method to determne an approprate k value for LSI and CMC TABLE I THE COMARISON OF AVERAGE DIMENSIONAL REDUCTION RATIOS LSI LSI (textual) (vsual-textual) CMC (vsual-textual) Before After DR Fg 7 System nterface The retreval results contan four subtopcs of forest They are forest nsect, forest coverage n varous dstrcts, forest overvew and endangered forests Book nformaton are lsted for users convenence D Effcency We pcked up 50 keywords and executed the queres by adustng the number of the llustratons returned, we change the condtons for retreval so that the results are more comprehensve and persuasve From Table, we can tell that, compared wth the baselne, the semantc model we proposed almost has no effect n lowerng the performance of the retreval system on varous condtons TABLE II AVERAGE QUERY TIME ON 50 KEYWORDS 0 results 0 results 50 results Illustrator 34s 35s 35s Baselne 3s 33s 3s

8 Fg 7 Retreval results of the query word forest V CONCLUSION In ths paper, we present a semantc-based llustraton retreval system, Illustrator The man obectve of Illustrator system s to explot the vsual and textual nformaton to ad the retreval of llustratons and overcome the drawbacks of the tradtonal text-based retreval systems Our man contrbutons are summarzed as follows: Due to the drawbacks of the archtecture of text-based retreval system, we ntroduced a novel scheme of llustraton retreval and developed a retreval system, Illustrator In addton, we proposed a CMC model whch s exploted to capture the correlaton among dfferent modals of features of llustratons Moreover, we ntroduced a re-rank scheme whch dversfes the topcs n retreval results sgnfcantly The Experment results demonstrate the effectveness of Illustrator system n llustraton retreval ACKNOWLEDGEMENT The proect n our paper s supported by Chna- Amercan Dgtal Academc Lbrary (CADAL) proect (No CADAL00400) and the H-Tech Research and Development rogram (863) of Chna (No 004AA900) Journal of Amercan Socety of Infomaton Scence and Technology, 4(6): [6] Hu, YH, L, H, Cao, YB, Meyerzon, D and Zheng, QH, Automatc Extracton of Ttle from General Documents usng Machne Learnng, In roceedngs of the 5th ACM/IEEE-CS ont conference on Dgtal lbrares, 45-54, 005 [7] Hu, YH, Xn, GM, Song, RH, Hu, G, Sh, SM, Cao, YB, and L, H Ttle extracton from bodes of HTML documents and ts applcaton to web page retreval, In proceedngs of the 8th annual nternatonal ACM SIGIR conference on Research and development n nformaton retreval, 50-57, 005 [8] L, DG, Dmtrova, N, L, MK, Seth, IK, Multmeda Content rocessng through Cross-modal Assocaton roceedngs of th ACM Internatonal Conference on Multmeda, Berkeley, Calforna, USA, p604-6, 003 [9] an, JY, Yang, HJ, Faloutsos, C and Duygulu,, Automatc multmeda cross-modal correlaton dscovery, In roceedngs of the tenth ACM SIGKDD nternatonal conference on Knowledge dscovery and data mnng, , 004 [0] Solas, G, d Alché-Buc, F, Support Machne Learnng Based on Semantc Kernel for Text Categorzaton roceedngs of the Internatonal Jont Conference on Neural Network (IJCNN), 000 [] Tan, YH, Huang, TJ, Gao, W, Explotng mult-context analyss n semantc mage classfcaton Journal of Zheang Unversty SCIENCE, Vol6A,No,pp68-83, 005 [] Zhao, R, Grosky, WI, Narrowng the semantc gap mproved text-based Web document retreval usng vsual features IEEE Trans Multmeda, 4():89-00, 00 REFERENCES [] Borowsk, M, Bröcker, L, Hesterkamp, S, Löffler, J Structurng the Vsual Content of Dgtal Lbrares usng CBIR Systems, In proceedngs of IEEE Internatonal Conference on Informaton, 88-93, 000 [] Casca, ML, Seth, S and Sclaroff, S, Combnng Textual and Vsual Cues for Content-Based Image Retreval on the World Wde Web, In roceedngs of the IEEE Workshop on Content - Based Access of Image and Vdeo Lbrares, 998 [3] Cohen, SD and Gubas, LJ, Shape-based Illustraton Indexng and Retreval - Some Frst Steps, In roceedngs of the ARA Image Understandng Workshop, 09-, February 996 [4] Crstann, N, Shawe-Talyor, J, Lodh, H, Latent semantc kernels Journal of Intellgent Informaton Systems, 8(/3):7-5, 00 [5] Deerwester, S, Dumas, ST, Furnas, GW, Landauer, TK, Harshman, R, Indexng by latent semantc analyss

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