1. Introduction. Abstract

Transcription

1 Automati Ontology Derivation Uing Clutering for Image Claifiation 1 Latifur Khan and Lei Wang Department of Computer Siene Univerity of Texa at Dalla, TX [lkhan, leiwang]@utdalla.edu Abtrat Tehnology in the field of digital media generate huge amount of non-textual information, audio, video, and image, along with more familiar textual information. The potential for exhange and retrieval of information i vat and daunting. The key problem in ahieving effiient and uer-friendly retrieval in the domain of image i the development of a earh mehanim to guarantee delivery of minimal irrelevant information (high preiion) while inuring that relevant information i not overlooked (high reall). The traditional olution to the problem of image retrieval employ ontentbaed earh tehnique baed on olor, texture or hape feature. The traditional olution work well in performing earhe in whih the uer peifie image ontaining a ample objet, or a ample textural pattern, in whih the objet or pattern i indexed. One an overome thi retrition by indexing image aording to meaning rather than objet that appear in image, although thi will entail a way of onverting objet to meaning. We have olved thi problem of reating a meaning baed index truture through the deign and implementation of a onept-baed model uing domain dependent ontologie. An ontology i a olletion of onept and their interrelationhip whih provide an abtrat view of an appliation domain. With regard to onverting objet to meaning the key iue i to identify appropriate onept that both deribe and identify image. We propoe a new mehanim that an generate ontologie automatially in order to make our approah alable. To ahieve thi we propoe a method for the automati ontrution of ontologie baed on lutering and a vetor pae model. Similarity of image i baed on imilarity of objet that appear in image. For objet imilarity meaure, we onider the ombination of olor and hape imilarity together. 1. Introdution The development of tehnology in the field of digital media generate huge amount of non-textual information, uh a audio, video, and image, a well a more familiar textual information. The potential for the exhange and retrieval of information i vat, and at time daunting. In general, uer an be eaily overwhelmed by the amount of information available via eletroni mean. The need for uer-utomized information eletion i lear. The tranfer of irrelevant information in the form of doument (e.g. text, audio, video) retrieved by an information retrieval ytem and whih are of no ue to the uer wate network bandwidth and frutrate uer. Thi ondition i a reult of inauraie in the repreentation of the doument in the databae, a well a onfuion and impreiion in uer querie, ine uer are frequently unable to expre their need effiiently and aurately. Thee fator ontribute to the lo of information and to the proviion of irrelevant information. Therefore, the key problem to be addreed in information eletion in the domain of image i the development of a earh mehanim whih will guarantee the delivery of a minimum of irrelevant information (high preiion), a well a inuring that relevant information i not overlooked (high reall). 1 Thi tudy wa upported in part by gift from Sun and the National Siene Foundation grant NGS

2 Image onit of variou objet, eah of whih may be ued to effetively laify the image. The untrutured format of image tend to reit tandard ategorization and laifiation tehnique. Traditional ytem ued to tore and proe multimedia image provide no mean of automati laifiation. The ability of thee ytem to retrieve relevant doument baed on earh riteria ould be greatly inreaed if they were able to provide an aurate/emanti deription of an image baed on image ontent. The traditional olution to the problem of image retrieval employ ontent-baed earh tehnique baed on olor, hitogram, texture or hape feature. The traditional olution work well in performing earhe in whih the uer peifie image ontaining a ample objet, or a ample textural pattern. Should a uer ak for an image depiting a baketball game, the reult beome le aurate. Thi i due to the fat that though an image may ontain a baketball, it doe not neearily depit a baketball game. In order to overome the hortoming of traditional tehnique in reponding to image laifiation we have deigned and implemented a onept-baed model uing ontologie. Thi model, whih employ a domain dependent ontology, i preented in thi paper. An ontology i a olletion of onept and their interrelationhip, whih an olletively provide an abtrat view of an appliation domain. In our ytem we would like to addre two ditint quetion: the extration of the emanti onept from the image and the ontrution of an ontology. With regard to the firt problem, the extration of emanti onept, the key iue i to identify appropriate onept that deribe and identify image. We would like to make ure that irrelevant onept will not be aoiated and mathed, and that relevant onept will not be diarded. In other word, it i important to inure that high preiion and high reall will be preerved during onept eletion. To the bet of our knowledge there are no attempt to onnet image and onept through the ue of ontologie in any traditional image retrieval ytem. We propoe an automati mehanim for the eletion of thee onept [16]. With regard to the eond problem, we propoe a new method for the automati ontrution of ontologie baed on lutering and vetor pae model. It i important to note that imilarity of image i baed on imilarity of objet that appear in image. In addition, objet imilarity take into aount not only olor or hape, but both. Our method ontrut ontologie automatially in bottom up fahion. For thi, we firt ontrut a hierarhy uing ome lutering algorithm. Reall that if doument are imilar to eah other in ontent they will be aoiated with the ame onept in ontology. After objet detetion, for eah objet, we extrat olor and hape information and expre them uing vetor. Then onidering both olor and hape fator, we an alulate the imilarity between objet applying vetor pae model. In ontologie eah onept i deribed by a et of feature (objet). Thu, before image lutering, we luter objet aording to imilaritie between objet and aign a weight for eah objet luter. Next, we ontrut a vetor for eah image baed on weight of objet luter and alulate imilaritie between image uing vetor pae model. Finally, baed on image imilaritie, we luter image and build ontology hierarhy uing hierarhy agglomerative lutering algorithm. Setion of thi paper diue work related to ontent-baed image retrieval and ontologie for ue in image retrieval, a well a the urrent ytem ued for image proeing. Setion 3 deribe ontologie, and how they peify interrelationhip among onept that help draw meaningful onluion about image. Setion 4 deribe outline of our approah. Setion 5 preent elaborately our approah to luter image and build ontology hierarhy. Setion 6 preent preliminary reult of our approah. Setion 7 preent our onluion and poible area of future work.

3 . Related Work Several ytem exit today that attempt to laify image baed on their ontent. Sueful laifiation of an image and it ontent relate diretly to how well relevant image may be retrieved when a earh i preformed. Mot image toring ytem uh a QBIC and ViualSEEK limit laifiation mehanim to deribing an image baed on metadata uh a olor hitogram, texture, or hape feature [, 8]. Thee ytem have high ue in performing earhe in whih the uer peifie image ontaining a ample objet, or a ample texture pattern. Should a uer ak for an image depiting a baketball game, the reult beome le aurate. Thi i due to the fat that though an image may ontain a baketball, it doe not depit a baketball game. Sytem that only ontain metadata regarding only olor and hape feature ontained in an image annot provide an aurate laifiation of the entire image. Other ytem attempt to provide image with a more preie deription by analyzing other element urrounding the image, uh a aption [9, 10], or HTML tag on web page [1]. Thee ytem ue thi information to help laify the image and give it a meaningful deription. Thi approah, tied together with metadata on image uh a texture, and olor ampling ha the potential to yield high preiion reult in image laifiation. Examining the textual deription aoiated with an image provide additional information that may be ued to help better laify the image. Unfortunately, thi approah doe not take into aount the onnetion among individual objet preent in a ample image. Suh onnetion provide ueful information in the form of relationhip among objet preent in the image, whih ould be ued to help laify the image ontent. For the ontrution of ontologie, only a few automati method are propoed [4, 6, 7]. Elliman et al. [7] propoe a method for ontruting ontologie to repreent a et of web page on a peified ite. Self organizing map i ued to ontrut hierarhy. In our ae we modify elf organizing tree and label node in the hierarhy. Bodner et al. [4] propoe a method to ontrut hierarhy baed on tatitial method (frequeny of word). Hoothe et al. [6] propoe variou lutering tehnique to view text doument with the help of ontologie. Note that a et of hierarhie will be ontruted for multiple view only; not for ontology ontrution purpoe. Furthermore, all thee ontology ontrution are done in text domain; however, we addre thi problem in the image domain. In our ytem, ontology erve a a taxonomy where imilar image are grouped together [3, 16]. Thi imilarity i not baed only on olor or hape but both along with finer grain (i.e., individual objet) rather than oarer grain (i.e., entire image). For example, a football game image may ontain green field, goalpot, and football objet. An image ontaining only a football would be milaified a a football game baed on olor imilarity analyi. On the other hand, hape imilarity may alo milaify image. Baed on only hape imilarity we may identify a baketball a a football. Therefore, neither olor nor hape baed imilarity i adequate to laify image. We need to ombine thee two imilaritie together to undertand emanti meaning of image. Therefore, to laify image effetively, we need a knowledgebae where olor and hape feature of eah ategory will be maintained. In our ae ontology erve a a knowledgebae; it ontain a et of onept where eah onept orrepond a ategory. And eah ategory ontain a et of image by haring a et of imilar objet. Here image imilarity i determined by objet imilarity baed on the ombination of olor and hape. One may argue that our ontology generation i baed on lutering apet of the problem. However, thi lutering group imilar image baed on emanti meaning. Thu, onept-baed lutering tehnique ha been employed.

4 3. Ontologie An ontology i a peifiation of an abtrat, implified view of the world that we wih to repreent for ome purpoe. Therefore, an ontology define a et of repreentational term that we all onept. Inter-relationhip among thee onept deribe a target world. An ontology an be ontruted in two way, domain dependent and generi. CYC, WordNet, and Senu are example of generi ontologie. For our purpoe, we hooe a domain-dependent ontology. A domain-dependent ontology provide onept in a fine grain, while generi ontologie provide onept in oarer grain. The finegrained onept allow u to determine peifi relationhip among feature in image that may be ued to effetively laify thoe image. Figure 1 illutrate an example ontology for the port domain [5]. Thi ontology may be obtained from generi port terminology and domain expert. The ontology i deribed by a direted ayli graph (DAG). Here, eah node in the DAG repreent a onept. In general, eah onept in the ontology ontain a label name and feature vetor. A feature vetor i imply a et of feature and their weight. Eah feature may repreent an objet of an image, uh a a baketball, a goalpot or a baeball. Note alo that thi label name onneted to the feature i unique in the ontology. Furthermore, thi label name i ued to erve a an aoiation of onept to image. The onept of football may be further expanded to objet preent in a football game (i.e. the feature of the onept). For intane, a green field, goalpot, and football player would indiate the image i a football game. Should only one or two of the feature ommon to a football game (a peified in the ontology) be preent, a le peifi laifiation of the image would be given. In other word, a more generi onept will be aigned to the image. An image ontaining only a football would be laified a an image ontaining a football, not a a football game. Furthermore, the weight of eah feature of a onept may not be equal. In other word, for a partiular onept ome feature may erve a more diriminating a ompared to ome other; it will be aigned higher weight. For example, in the onept of a game of football the weight of goalpot feature i higher than the weight of the feature, green field. In ontologie, onept are interonneted by mean of inter-relationhip. If there i a inter-relationhip R, between onept C i and C j, then there i alo a inter-relationhip R between onept C j and C i. In Figure 1, inter-relationhip are repreented by labeled ar/link. Three kind of inter-relationhip are ued to reate our ontology: IS-A, Intane-of, and Part-of. Thee orrepond to key abtration primitive in objet-baed and emanti data model [1]. Figure 1: A Portion of an ontology for the Sport Domain

5 4. Propoed Sytem Our ytem irumvent the low preiion laifiation tehnique of other ytem by examining the atual objet within an image and uing them to diover relationhip that reveal information ueful in laifying the entire image. The onept behind thee relationhip are held in our knowledge bae of domain-dependant ontologie a deribed in etion 3. We now outline the tep taken to uefully proe and laify an input image preented to our ytem. To onvert objet to meaning or automati ontology ontrution, we need to identify all objet boundarie aurately (box 1 in Figure 1) that appear in image [3]. In thi earlier work [3] we propoe an automati alable objet boundary detetion algorithm baed on edge detetion and region growing tehnique. Our algorithm work in three tage. Firt, we detet all edge pixel in image and divide pixel into two et, edge pixel and region pixel et. Seond, we grow a region from the region pixel et urrounded by edge taken from the edge pixel et. Finally, we may merge adjaent region uing an adjaeny graph to avoid over egmentation of region and to detet boundary of objet aurately. After detetion of objet boundarie, we would like to build ontologie automatially (box in Figure ). Our method ontrut ontologie automatially in bottom up fahion. For thi, we firt ontrut a hierarhy uing ome lutering algorithm. Reall that if doument are imilar to eah other in ontent they will be aoiated with the ame onept in ontology. After objet detetion, for eah objet, we extrat olor and hape information and expre them uing vetor. Then onidering both olor and hape fator, we an alulate the imilarity between objet applying vetor pae model. In ontologie eah onept i deribed by a et of feature (objet). Thu, before image lutering, we luter objet aording to imilaritie between objet and aign a weight for eah objet luter. Next, we ontrut a vetor for eah image baed on weight of objet luter and alulate imilaritie between image uing vetor pae model. Finally, baed on image imilaritie, we luter image and build ontology hierarhy uing hierarhy agglomerative lutering algorithm. Here a et of image will be ued for ontrution of hierarhy. Thu, after ontrution of hierarhy, all emantially imilar image baed on objet imilarity will be grouped together. Furthermore, to laify a query image, firt we egment image into objet baed on our objet boundary detetion algorithm. Next, we determine imilarity between objet that appear in thi query image and objet that appear in onept entroid image uing vetor pae model (box 3 in Figure ), and hooe the mot imilar one [16]. After the objet have been identified, their identifiation are fed into a onept eletion module (box 4 in Figure ). The ontologie ue thi information to provide a meaningful deription of the image by eleting onept baed on image ontent (i.e., individual objet within the image). In our earlier work [15] onept eletion mehanim inlude a novel, alable diambiguation algorithm uing a domain peifi ontology. Thi algorithm will prune irrelevant onept while allowing relevant onept to beome aoiated with image. For example, it i poible an image may be laified a both an NBA baketball game and a ollege baketball game at the ame time. However, we employ the heuriti-baed pruning tehnique to narrow down the eletion of onept. When the pruning algorithm omplete the eleted onept will be orted baed on their ranking in deending order. The onept label will then tell whih ategory the image belong to.

6 Training Image (TI) Objet Detetion (I) (I) Objet in TI Ontology Contrution Objet aoiated in a onept Similarity meaure of objet uing Vetor Spae Model Objet of QI appeared in onept Conept Seletion Query Image (QI) Objet in QI Figure. Flow of Our Sytem 5. Ontology Derivation Our goal i to ontrut ontologie automatially. For thi, we would like to build a hierarhy from a et of image in a bottom up fahion. Note that we do not want to luter image baed on the imilarity of olor. Hene, imilar image will be lutered if they hare imilar objet. Furthermore, ome objet may arry more weight a ompared to other objet that appear in image, imilar to the way keyword behave in doument. For thi, we need to aign weight to objet that appear in image. To determine the weight of objet we firt luter objet baed on imilarity of olor and hape. Next, we determine weight of objet that appear in image baed on term frequeny and invere doument frequeny, imilar to IR. Eah image will then be repreented by a vetor, where a vetor ontain a et of weight that orrepond to the importane of the objet that appear in the image. Uing thi vetor we will ontrut an image hierarhy uing agglomerative lutering. We will diu eah tage elaborately in the following etion: 5.1 Objet Clutering By applying egmentation tehnique, we egment image into objet. Let u aume we have N image in a databae. After egmentation, we detet M number of objet in total. Then we luter thee M objet into a et of group (ay t) C 1, C,. C t aording to imilaritie between objet. Here, objet imilarity will be baed on the ombination of olor imilarity and hape imilarity of individual objet. Thi i beaue if viual feature of objet uh a olor and hape are imilar it i very poible that thee objet have imilar emanti meaning. Thu, we firt introdue our olor imilarity meaure, next our hape imilarity meaure, and then the ombination of both Color Similarity Meaure To ompute olor imilarity, we firt ontrut a vetor for an objet oniting of a et of value of hitogram bin. For eah hitogram bin (olor ode), we determine how many pixel of thi partiular objet appear in the hitogram bin. Thu, for objet i a vetor Vi (v 1,i, v,i,.. v p,i, v k,i ) will be ontruted to expre the olor hitogram. In the vetor eah element repreent the perentage of pixel whoe hue value loate in peifi interval. For example, v p,i i the perentage of pixel whoe hue value are between * π * p / k and * π * (p + 1) / k in objet i, beaue the range of hue value in HSI olor pae i from 0 to * π. Furthermore, we only onider hue omponent for imilarity

7 meaure whih i adequate. Now, we an determine the degree of olor imilarity (im (i, ) between objet i and objet j baed on oine produt. Thu, ρ ρ k i j v p = p i v 1, p, j im ( i, = ρ ρ = k k i j v p = p i v 1, p = 1 p, j (1) The value of k affet the auray of olor imilarity. Along with inreaing of k, auray will be alo inreaed. However, it will be omputationally expenive Shape Similarity Meaure The hape imilarity i little bit more ompliated. To upport imilarity querie, Lu et al. [14] introdue fixed reolution (FR) repreentation method. We adopt thi idea, but make ome hange during implementation. Firt, we need to find the major axi that i the longet line joining two point on the boundary. For normalization purpoe we rotate an angle θ around ma entroid of objet to make the major axi to be parallel to x-axi and keep the entroid above the major axi. The reaon for normalization i to make it invariant to rotation [13]. The oordinate of the entroid are a follow: σ(x,y) i urfae denity funtion. () After normalization, we divide the objet into q * q grid, whih i jut big enough to over the entire objet, and overlaid on the objet where q i an integer number. The ize of eah ell i ame. Then we define a hape vetor for objet i, Ui (u 1,i, u,i,.. u p,i, u q,i ) of q ize. Eah element in the vetor tand for the perentage of pixel in the orreponding ell. The higher the q value, the higher the auray. Of oure, it will then be omputationally expenive. Finally, we determine hape imilarity (im (i, ) between two objet i and j uing oine imilarity. Thu, im ρ ρ q i j u p = 1 p, i u p, j ( i, = ρ ρ = i q q j u p = 1 p, i u p = 1 (3) Combined Similarity Now, we would like to determine imilarity between two objet baed on olor imilarity and hape imilarity. Uing Equation and 3, imilarity (im (i, ) between objet i and objet j i a follow: im( i, = im weight + weight ( i, weight = 1 + im ( i, weight (4) Weight i the weight of olor and weight i the weight of hape. When it i poible that one type of imilarity may be more important a ompared to another we need to ue weight. In the urrent ae, we aume that both weight and weight are equally important (=0.5). To ontrut objet luter we ue a threhold T obj. If imilarity between two objet i greater than T obj, then the two objet an be in the ame group. In other word, imilarity between eah pair of objet in the ame group mut be greater than T obj. Furthermore, it i poible for an objet to appear in more than one group. It i important to note that a group oniting of a et of objet orrepond to a keyword a oppoed to eah individual objet orreponding to a keyword. Thi i beaue in IR math i well defined; in multimedia, math i ill defined (imilarity). p, j

8 5. Vetor Model for Image Image lutering i baed on image imilarity. To alulate image imilarity, we ontrut an image, l vetor W l (w 1,l, w,l w i,l, w t,l ) rather than meauring imilarity baed on olor. W i,l i the weight of objet luter C i in image l. In thi vetor we keep the weight of eah group. Thu, the ize of vetor W i ame a the total number of objet luter (=t). It i poible that the weight of a group may be zero. Thi i beaue no objet of an image may be a partiipant of that group during lutering. To determine an image vetor, we adopt the idea from the area of information retrieval [39]. Here, image orrepond to doument, and objet luter orrepond to term (keyword). Let N be the total number of image and n i be the number of image in whih objet of luter C i appear. We define the normalized term frequeny f i,j : f i, l freqi, l max freq = (5) h h, l The freq i,l i the number of time luter C i appear in the image l. Similarly, for max freq h,l we determine ourrene of a luter that appear in image, l; for thi luter we get maximum ourrene among all luter. We alo define an invere doument frequeny idf i for C i : idf i N log n = (6) i Conidering the above two fator, we have the weight of luter: w f idf i, l i, l i = i, l f N log n = (7) i After omputing image vetor, we get imilarity between any two image uing oine imilarity: ρ ρ t i j w h = h i w 1, h, j im img ( i, = ρ ρ = t t i j (8) w h = h i w 1, h = 1 h, j 5.3 Hierarhial Clutering of Image Uing the above method, we an alulate image imilarity between eah pair of image. Then we apply a hierarhial agglomerative lutering algorithm (HAC) to ontrut hierarhy. The HAC algorithm i a ommonly employed laial hierarhal lutering algorithm. The reult of HAC i a dendrogram repreenting the neted grouping of image. The general HAC algorithm i a follow: 1) Put eah image into a ingleton luter, ompute a lit of inter luter ditane for all ingleton luter, then ort the lit in aending order. ) Find the pair of luter with the mot imilar, merge them into one luter and alulate the imilarity between the new luter and the remaining luter. 3) While there i more than one luter remaining, go to tep, otherwie top. Baed on the alulation of imilarity between the non-ingleton luter a variety of hierarhial agglomerative tehnique have been propoed. Single-link, omplete-link and groupaverage-link lutering are ommonly ued. In the ingle-link luter the imilarity between two luter i the maximum imilarity of all pair of doument whih are in different luter. In the omplete-link luter, the imilarity between two luter i the minimum imilarity of all pair of doument whih are in different luter. In Group-Average-link lutering the imilarity between two luter i the mean imilarity of all pair of ingleton whih are in different luter.

9 In the hierarhy, a node will be repreented by a repreentative image whih i mot imilar to the node vetor. It i important to note that variou type of inter-relationhip between node are blurred in our ontologie; ertain type of interonnetion are ignored. Thi i beaue our prime onern i to failitate information eletion rather than to dedut new knowledge. 6. Experimental Preliminary Reult The purpoe of the experiment i to tet the auray of the lutering. We have ued a et of image belonging to 6 different ategorie, uh a baketball game, baeball game, bat, football game, goggle and playground. We have then ontruted hierarhy baed on the theory diued in Setion 5. We have hoen Group-Average-Link lutering in HAC. We have et k=1 (ee Equation 1), and T obj =0.7. To meaure the quality of a luter, we ue preiion, reall, and E meaure [33]. Reall i the ratio of relevant image to total image for a given ategory. Preiion i the ratio of relevant image to image that appear in a luter for a given ategory. E meaure i defined a follow: E( p, r) = 1 (9) 1 p + 1 r Where p and r are the Preiion and Reall of a luter. Note that E (p, r) i imply one minu harmoni mean of the preiion and reall; E (p, r) range from 0 to 1 where E (p, r) =0 orrepond to perfet preiion and reall, and E (p, r) orrepond to zero preiion and reall. Thu, the maller the E meaure value the better the quality of a luter. In Figure 3 X axi repreent different ategorie and the Y axi repreent p, r and E. We have oberved that preiion and reall are higher for imple image (e.g., play ground) a ompared to preiion and reall of omplex image (e.g., baeball game, football game). Thu, E value i lower for imple image (e.g., play ground) a ompared to E value of omplex image (e.g., baeball game, football game). preiion p,r and E reall ommon E meaure baketball football baeball bat goggle playground Categorie 7. Conluion and Future Work Figure 3. Cluter Quality for Different Categorie In thi paper we have propoed a potentially powerful and novel approah for the automati ontrution of ontology. The rux of our innovation i the development of a hierarhy baed on objet imilarity uing a vetor pae model. Furthermore, to determine objet imilarity we have ombined both olor imilarity and hape imilarity. To illutrate the effetivene of our algorithm in automati

10 image laifiation, we implement a very bai ytem aimed at the laifiation of image in the port domain. For developing a hierarhy, we have ued an agglomerative lutering algorithm that ontrut hierarhie from bottom to up. We would like to extend thi work in the following diretion. Firt, we would like to do more experimentation with the lutering tehnique. Next, we would like to addre thi kind of ontology ontrution in variou domain. Referene [1] G. Alan and D. MLeod, Semanti Heterogeneity Reolution in Federated Databae by Metadata Implantation and Stepwie Evolution, The International Journal on Very Large Databae, Vol. 18, No., Otober [] R. Barber, W. Equitz, C. Falouto, M. Fikner, W. Niblak, D. Petkovi, and P. Yanker, Query by Content for Large On-Line Image Colletion, IEEE Journal, [3] Lei Wang, Latifur Khan, and Caey Breen, Objet Boundary Detetion for Ontology-baed Image Claifiation, Third International Workhop on Multimedia Data Mining, Edmonton, Alberta, Canada, July 00. [4] R. Bodner and F. Song, Knowledge-baed Approahe to Query Expanion in Information Retrieval, in Pro. of Advane in Artifiial Intelligene, pp , New York, Springer. [5] ESPN CLASSIC, [6] Dave Elliman, J. Rafael G. Pulido. Automati Derivation of On-line Doument Ontology. International Workhop on Mehanim for Enterprie Integration: From Objet to Ontology (MERIT 001) 15th European Conferene on Objet Oriented Programming, Budapet, Hungary, Jun 001. [7] A. Hotho, A. Mädhe, A., S. Staab, Ontology-baed Text Clutering, Workhop Text Learning: Beyond Superviion, 001. [8] A. Pentland, R.W. Piard, S. Slaroff, Photobook: Tool for Content-Baed Manipulation of Image Databae, in Pro. of Storage and Retrieval for Image and Video Databae II, Volume 185, pp , Bellingham, WA, [9] N. Row, and B. Frew, Automati Claifiation of Objet in Captioned Depitive Photograph for Retrieval, Intelligent Multimedia Information Retrieval, Chapter 7, M. Maybury, AAAI Pre, [10] A. F. Smeaton and A. Quigley, Experiment on Uing Semanti Ditane between Word in Image Caption Retrieval, in Pro. of The Nineteenth Annual International ACM SIGIR Conferene on Reearh and Development in Information Retrieval, [11] C.J. van Rijbergen. Information Retrieval. Butterworth, London, [1] C. Frankel, M.J. Swain and V. Athito, WebSeer: An Image Searh Engine for the World Wide Web, Univerity of Chiago Tehnial Report TR-96-14, July 31, [13] Chakrabarti, K., Ortega-Binderberger, M., Porkaew, K & Mehrotra, S. (000) Similar hape retrieval in MARS. Proeeding of IEEE International Conferene on Multimedia and Expo. [14] G. Lu and A. Sajjanhar, Region-baed hape repreentation and imilarity meaure uitable for ontent-baed image retrieval. Springer Verlag Multimedia Sytem, [15] Riardo Baeza-Yate, Berthier Ribeiro-Neto, Modern Information Retrieval, ISBN X, [16] C. Breen, L. Khan, A. Ponnuamy, and L. Wang, Ontology-baed Image Claifiation Uing Neural Network, Pro. of SPIE Internet Multimedia Management Sytem III, pp , Boton, MA, July, 00.