Object-Based Techniques for Image Retrieval

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1 54 Zhang, Gao, & Luo Chapter VII Object-Based Technques for Image Retreval Y. J. Zhang, Tsnghua Unversty, Chna Y. Y. Gao, Tsnghua Unversty, Chna Y. Luo, Tsnghua Unversty, Chna ABSTRACT To overcome the drawback of usng only low-level features for the descrpton of mage content and to fll the gap between the perceptual property and semantc meanng, ths chapter presents an object-based scheme and some object level technques for mage retreval. Accordng to a mult-layer descrpton model, mages are analyzed n dfferent levels for progressve understandng, and ths procedure helps to gan comprehensve representatons of the objects n mages. The man propulson of the chapter ncludes a mult-layer descrpton model that descrbes the mage content wth a herarchcal structure; an effcent regon-based scheme for meanngful nformaton extracton; a combned feature set to represent the mage at a vsual percepton level; an teratve tranng-and-testng procedure for object regon recognton; a decson functon for reflectng common contents n object descrpton and a combned feature and object matchng process, as well as a selfadaptve relevance feedback that could work wth or wthout memory.

2 Object-Based Technques for Image Retreval 55 Wth the proposed technques, a prototype retreval system has been mplemented. Real retreval experments have been conducted; results show that the object-based scheme s qute effcent and the performance of object level technques have been confrmed. INTRODUCTION Fast technque advancement and the rapd nformaton ncrements mark the new century. Along wth the progress of magng modalty and the wde utlty of dgtal mage n varous felds, many potental content producers have emerged, and many mage databases have been bult. How to quckly access and manage these large, both n the sense of nformaton contents and data volume, databases has become an urgent problem to solve. In the past 0 years, mage retreval technques have drawn much nterest, and content-based mage retreval (CBIR) technques are proposed n ths context to search nformaton from mage databases quckly and effcently (Kato, 99). Wth the advantage of comprehensve descrptons of mage contents and consstence to human vsual percepton, research n ths drecton s consdered as one of the hottest research ponts n the new century. Though many efforts have been put on CBIR, many technques have been proposed and many prototype systems have been developed, the problems n retrevng mages accordng to mage content are far from beng solved. Most of current technques and systems for mage retreval just take nto consderaton low-level vsual features, such as color and texture of mage, or shape of objects and spatal relatonshps among dfferent regons n mages, to descrbe mage contents. However, there s a consderable dfference between the users nterest n realty, and the mage contents descrbed by only usng the above low-level mage features. In other words, there s a large gap between such mage content descrpton based on low-level features and that of human bengs understandng. As a result, these low-level feature-based approaches often lead to unsatsfyng queryng results n many cases. In ths chapter, a general scheme and some object-based technques are proposed to effcently fll the gap between the low-level feature and hgh-level semantc descrpton of mages. Ths s n the hope of makng content-based mage retreval more lke ts real meanng, nstead of just consderng the vsual percepton. Throughout ths chapter, several technques are proposed, and all these technques are gathered together nto an object-based framework for mage retreval. The proposed structure of ths chapter s as follows: Background, () Extracton of Interestng Regons, () Object-level Processng, (3) Self-Adaptve Relevance Feedback; Man Thrust of Chapter, () Mult-Layer Descrpton Model, () Meanngful Regon Extracton, (3) Perceptual Feature Extracton, (4) Object Recognton, (5) Object Descrpton and Matchng, (6) Experments and Dscussons; Drecton of Future Research; and Concluson. BACKGROUND In content-based mage retreval, how to represent and descrbe the content of an mage s a central ssue. Many methods have been used, three broad categores are: synthetc, semantc and semotc (Bmbo, 999; Valaya, 000; Djeraba, 00). The

3 56 Zhang, Gao, & Luo progress s enormous, however, due to the nature of ths problem, many challengng tasks arose. Some of them are brefly dscussed n the followng. Extracton of Interestng Regons Wth the advantage of comprehensve descrptons of mage contents, contentbased mage retreval has become one of the hottest research aspects and many practcal retreval systems have been developed, such as QBIC (Lee, 994), Photobook (Pentland, 996), VsualSEEK (Smth, 996), CAFIIR (Wu, 997), and FourEyes (Mnka, 997), et al. However, most of the above mage retreval systems use low-level mage features, such as color, texture, and shape, etc., to represent mage contents drectly. In fact, there s a consderable gap between semantcs of mages that the user s really nterested n and the mage content representaton by the above low-level mage features. Therefore, t s not surprsng that the query results are often not much satsfyng. To closely capture the content of mages and to effcently represent the nformaton of mages, an object-based framework would be requred. However, precse object segmentaton n many cases s stll beyond the capablty of current computer technques. Much work has been done n decomposng an mage nto regons wth unform low-level features such as color or texture. However, such a regon may have few semantc meanngs. In ths chapter, meanngful regon extracton s proposed. The meanngful regon s at the ntermedate level between the orgnal mage and the nterestng object of the mage. Ths level s an effectve vsual level for the representaton of mages, accordng to humans vsual acuty. In addton, a meanngful regon should be grouped n a hgher feature space to have some sutable semantc meanng. Semantc meanngs can be extracted from the meanngful regon automatcally and further object descrptons could be obtaned wth the help of a knowledge database. Object-Level Processng In the feld of content-based mage retreval, a new research trend s to use hghlevel descrptons, nstead of low-level features, n the match process. How to extract hgh-level descrptons from mages and to fll the gap between the low-level features and human bengs understandng of mage contents drectly are crtcal. One promsng technque to solve ths problem s to descrbe the whole mage wth a herarchcal structure to reach progressve mage analyss (Castell, 998; James, 999; Hong, 999). The contents of mages can be represented n dfferent levels (Amr, 998), such as the three-level content representaton, ncludng feature level content, object level content and scene level content (Hong, 999), and the fve-level representaton, ncludng regon level, perceptual regon level, object part level, object level and scene level (James, 999). Among dfferent structural levels, object level s consdered the key lnkng the lower feature level and the hgher semantc level. However, many exstng methods treat the object level wth tradtonal mage segmentaton and pattern recognton procedure, whch would not be very feasble and could not greatly smulate human bengs understandng of mage contents. To extract semantcs from an mage, a number of current methods try to map lowlevel vsual features to hgh-level semantcs. In other words, to fll the semantc gap, one makes the system work wth low-level features whle the user puts more hgh-level

4 Object-Based Technques for Image Retreval 57 knowledge n Zhou (00). Two typcal methods are the optmzng query request by relevance feedback and semantc vsual template (Chang, 998) and the usng of nteractve nterface to progressvely understand the contents of mage (Castell, 998). In ths chapter, an object-level approach for content descrpton and matchng s proposed based on a mult-layer descrpton model. A prototype system for retrevng mages n object-level s also mplemented. Dfferng from many other retreval systems, there s no need for the user to provde sample mages or sketch mages. The user could submt hs queryng demand by just tellng the system what knd of objects should be ncluded n the retreved mages, whch s called object queryng. Further, the user could requre expected spatal relatonshps among selected objects, whch s called relatonshp queryng. Experments wth real mages have been conducted usng ths system, and the effectveness of object-based retreval has been justfed. Self-Adaptve Relevance Feedback It s generally accepted that hgh-level mage features are crucal to mprove the performance of content-based mage retreval up to so-called semantc-based queryng. Among the related technques, relevance feedback has been pad a lot of attenton because t could combne the nformaton from the user. In practce, many methods have been proposed to reach the goal of relevance feedback (Ru, 998; Cocca, 999; Lee, 999). In ths way, through the selecton of relevant and non-relevant mages from the system nterface, the semantc relatonshps between mages are captured and embedded nto the system by splttng/mergng mage clusters and updatng the correlaton matrx. There s a common pont of these methods where the relevance feedback s based drectly on the low-level mage features and the operaton of feedback s consdered the approach to hgh-level semantcs. However, relevance feedback s not exactly the representaton of mage semantcs but the way to refne the queryng results. Wthout a proper way to descrbe the mage semantcs, the functon of relevance feedback could not be exerted well. In ths chapter, a new scheme for relevance feedback s proposed, whch s ntegrated to a semantc-based mage retreval system. Unlke prevous methods for relevance feedback, ths scheme provdes a self-adaptve operaton. Based on mult-level mage content analyss, the relevant mages fed-back from the user could be automatcally analyzed n dfferent levels and thus, the ganed result would be used to determne the queryng scheme. In practce, to make the queryng more convenent to the user, the procedure of relevance feedback could be led wth memory or wthout memory. Expermental results show that the performance of the mage retreval could be greatly mproved through the self-adaptve relevance feedback, both n accuracy and effcency. MAIN THRUST OF THE CHAPTER Mult-Layer Descrpton Model How to descrbe mage contents s a key ssue n content-based mage retreval. In other words, among the technques for mage retreval, the mage content descrpton model would be a crucal one. Humans understandng of mage contents possesses some fuzzy characterstcs, whch ndcates that the tradtonal mage descrpton model based on low-level mage features (such as color, texture, shape, etc.) s not always consstent

5 58 Zhang, Gao, & Luo to human vsual percepton. The gap between low-level mage features and hgh-level mage semantcs makes the queryng results sometmes dsappontng. To mprove the performance of mage retreval, there s a strong tendency n the feld of mage retreval to analyze mages n a herarchcal way and to descrbe mage contents on a semantc level. A wdely accepted method for obtanng semantcs s to process the whole mage on dfferent levels, whch reflects the fuzzy characterstcs of mage contents (Hong, 999; James, 999). In the followng, a mult-layer descrpton model for mage descrpton s depcted (see Fgure ), whch can descrbe the mage content wth a herarchcal structure to reach progressve mage analyss and understandng (Gao, 000b). The whole procedure has been represented n four layers: orgnal mage layer, meanngful regon layer, vsual percepton layer and object layer. The descrpton for a hgher layer could be generated from the descrpton from the adjacent lower layer, and establshng the mage model s synchronously the procedure for progressve understandng of mage contents. These dfferent layers could provde dstnct nformaton of the mage content, so ths model s sutable to access from dfferent levels. In Fgure, the left part shows that the proposed mage model ncludes four layers, the mddle part shows the correspondng formulae for the four layers representaton, whle the rght part gves some presentaton examples of the four layers. In ths model, mage content s analyzed and represented n four layers. There s a context between adjacent layers that the representaton for the upper layer s drectly extracted from that for the lower layer. The frst step s to splt the orgnal mage nto several meanngful regons; each of them provdes certan semantcs n terms of human bengs understandng of mage contents. Then, proper features should be extracted from these meanngful regons to represent the mage content at a vsual percepton layer. In the nterest of followng up processng, such as object recognton, the mage features should be selected carefully. The automatc object recognton overcomes the dsadvantage of large overhead n manual labelng whle t throws off the drawback of nsuffcent Fgure : Mult-Layer Descrpton Model MULTI-LAYER FORMULA EXAMPLE Object Layer Relatonshp Determnaton Object Recognton Vsual Percepton Layer Feature Extracton Mnngful Regon Layer Pre-segmentaton Orgnal Image Layer OL VPL MRL OIL { TRs} F { F MC F WT F RD } l(x, y) f(x, y)

6 Object-Based Technques for Image Retreval 59 content nformaton representaton by usng only lower level mage features. Another mportant part of the object layer process s relatonshp determnaton, whch provdes more semantc nformaton among the dfferent objects of an mage. Based on the above statements, the mult-layer descrpton model (MDM) could be expressed by the followng formula: MDM { OIL, MRL, VPL, OL} () In Equaton (), OIL represents the lowest layer orgnal mage layer wth the orgnal mage data represented by f(x, y). MRL represents the labeled mage l(x, y) whch s the result of meanngful regon extracton (Luo, 00). VPL s the descrpton for the vsual percepton layer, and t contans three elements: F MC, FWT and F RD, representng Mxed Color Feature, Wavelet Package Texture Feature and Regon Descrptor, respectvely (Gao, 000a). One pont that should be notced s that the selecton of VPL s flexble and should be based on the mplementaton. OL s the representaton of object layer and t ncludes two components: T and R s. The former T s the result of object recognton to ndcate the attrbute of each extracted meanngful regon, for whch detaled dscussons would be gven n the followng. The latter Rs s a K K matrx to ndcate the spatal relatonshp between every two meanngful regons, wth K representng the number of the meanngful regons n the whole mage. In bref, all the four layers together form a bottom-up procedure (mplyng a herarchcal mage process) of mult-layer mage content analyss. Ths procedure ams at analyzng the mage n dfferent levels so that the mage content representaton could be obtaned step by step from low-level to hgh-level. In addton, ths procedure s also the bass of the proposed self-adaptve relevance feedback. Meanngful Regon Extracton Tradtonally, mage features are computed wth the whole mage, whch makes further meanngful content representaton and semantc extracton very dffcult. Here, a process of pre-segmentaton s suggested before further processes to extract dfferent nterest regons from the whole mage (Pauwels, 998). Dfferng from general mage segmentaton that leads to pxel-level precson, the goal of pre-segmentaton s only to roughly extract semantcally sgnfcant regons n mage. Accordng to human vsual percepton theory, durng vsual percepton and recognton process, human eyes move and successvely fxate on the most nformatve parts of the mage. These nformatve parts are named meanngful regons as they possess certan semantc meanngs. The delneaton of these meanngful regons wll help the followng percepton feature extracton. Flowchart The flowchart for roughly extractng meanngful regons n an mage s shown n Fgure. Dfferent modules wll be detaled n the followng subsectons, here an overvew s provded. Frst, the orgnal mages are classfed nto dfferent mage groups, accordng to ther complexty ndex. For smple mages wth a lower complexty ndex, the meanngful regons could be extracted by usng only hue nformaton. The mages wth very hgh

7 60 Zhang, Gao, & Luo Fgure : The Flowchart for Roughly Extractng Meanngful Regons Orgnal Image Nk > Nh Y Classfed as mage wthout vtal object Complexty Computaton Nl<Nk<Nh Nk < Nl Y Y Densty Functon Based Clusterng Hue-hstogram Based Segmentaton Postprocessng and Labelng complexty ndexes are exempted from further process, as the regons extracted from these mages are hard to recognzed as sgnfcant objects. For other mages, the extracton of meanngful regons wll be based on some hgh-level features deduced from low-level features, and a non-parametrc clusterng algorthm based on weghted densty functon wll be appled. Some mage post-processng technques are also employed such as nose removal and mergng very small regons that do not contan sgnfcant semantc meanng. Complexty Index The complexty ndex N k s determned by the followng: N N R ( ) k k () where N s the mage sze (pxel number), k s the sze of the neghborhood area consdered, R k () s the rato of the number of pxels labeled as the -th pxel s label n ts k-szed neghborhood. The complexty ndex N k s a functon of k, and for a gven k, N k s just a functon of the complexty of mages. The value of N k s bgger f the complexty of the mage s hgh. Accordng to the value of the complexty ndex, the mages can be classfed nto dfferent groups. Two lmtng thresholds, N l and N h, can be used. If N k < N l, the mage would be smple, and good segmentaton results could be obtaned by only usng huehstogram based segmentaton. For a large number of mages, the value of the complexty ndex would be moderate, N l < N k < N h, so a clusterng by usng multdmensonal feature analyss and n weghted-densty space would be necessary. If N k > N h, the mage would be too complex (composed of a large number of small regons) for further object recognton. Hue-Hstogram Based Segmentaton There are several color representaton schemes, among them the Hue-Saturaton- Intensty (HSI) color representaton. It s used because the HSI color space represents the human concept of color well and t s more sutable for the segmentaton of natural color mages (Zhang, 999). Usually, n the HSI color space, ntensty s used for texture analyss. The other two components have been used to extract the color nformaton. The saturaton s taken to

8 Object-Based Technques for Image Retreval 6 produce the mportance ndex, whch ndcates the weght of color nformaton. The hgher the saturaton, the more relable the color nformaton and the process based on color nformaton. The percepton of color by human eyes s manly obtaned from the hue component, so a hue-hstogram (weghted by saturaton) based mult-threshold selecton technque s employed for treatng smple mages. Texture Feature There s always very rch texture nformaton n natural mages and texture nformaton should be consdered n treatng the most of mages (Wouwer, 999). People can easly dfferentate dfferent objects accordng to ther texture. It has been shown that humans n the process use some perceptual texture features such as coarseness, contrast and drecton to dstngush between textured mages or regons. Among these, coarseness s the most fundamental property and, n a certan sense, t s the coarseness that determnes the texture. Accordng to the coarseness ndex, whether an mage s rch n texture nformaton can be judged (Karu, 996). Let C be the ndex of coarseness, the mage contans more texture nformaton f C s larger, and vce versa. In the mage ntensty space, let E(, j) = f pxel (, j) s a local extreme (ether row maxmum/mnmum or column maxmum/ mnmum) and E(, j) = 0 f the pxel s not, then: C N (, j) mage E(, j) (3) Coarseness estmaton can help the determnaton of the weght for texture nformaton. Densty Functon Perceptual salent regons n mages are presumed to have relatvely hgh data densty than the boundary area (Coleman, 979; Evertt, 993). Suppose an mage s represented by the data set {x R n, =,,, N}, convolutng t wth a un-model kernel functon K can produce the densty functon (Pauwels, 999): N f ( x) K ( x x ) (4) N where the kernel can be a Gaussan kernel K ( x) ( x exp n ) (5) x s an n-d vector, and n s the number of the features used.

9 6 Zhang, Gao, & Luo The densty space can be obtaned once havng the densty functon. The cluster n the densty space must be homogeneous n all the low-level feature space used (Hchem, 999). In the densty space, the local maxmum that represents one object n the mage can be computed by: F F n f F n 0 (6) At the begnnng, the cluster number could be overestmated by gvng a low. Then, an optmal clusterng can be obtaned by groupng the mage segments nto more meanngful regons. The dstance between the -th and j-th clusters can be defned by: d Ds(, j) W ( ) W ( j) (7) where d s the Mahalanobs dstance, s the varance of the scores on the lne from cluster and j, W s the sze of the cluster. Accordng to Ds(, j), two clusters wth mnmum dstance wll be merged. To control the clusterng procedure, an ndex S k, s to be determned: S K f ( k K t ) (8) Some K sub-regons n a cluster should be determned to fnd the local mnmum densty score f(t ). S k s small f there are several low-densty areas n a cluster. Ths means less smlar regons are grouped together. Wth a small cluster number, S k wll be decreasng. The summaton of S k and N k (as defned before) wll ndcate the optmal clusterng result (Luo, 00). Weghted Non-Parametrc Clusterng Early work n decomposng mages nto regons wth unform characterstcs often used features such as color, texture or edge etc. Though a regon can be defned as a set of homogeneous elements n the feature space, t may not have any semantc meanng. A meanngful regon s grouped by a set of elements homogeneous n a hgher level space, called Densty Space, that contans more complcated nformaton and more semantc meanng. Dfferent features have dfferent contrbutons to mage clusterng. For example, the hue nformaton s more relable f the mage s hghly color saturated. The large varance of one feature always ndcates that ths feature would dstngush dfferent objects clearly and t wll play an mportant role n mage segmentaton and object recognton. So the followng dynamc weghtng mechansm could be employed:

10 Object-Based Technques for Image Retreval 63 W d (9) where and are the standard devaton and the mean of feature sets. Ths weghtng mechansm permts us to utlze all the features effcently. By usng mult-dmensonal low-level feature analyss (color, texture, etc.), the relablty of dfferent features can be determned n order to adaptvely weght the contrbuton of each feature to the segmentaton process (Castagno, 998). After determnng the weght of dfferent features, a new-weghted non-parametrc clusterng algorthm n the densty space can be mplemented. Weghtng mechansms are used to control the nfluence of each data source n the combned classfcaton and to mprove the combned classfcaton accuracy. One example s shown n Fgure 3. Fgure (a) s an orgnal mage, Fgure (b) and (c) are the results obtaned by usng color feature and texture feature, respectvely, Fgure (d) shows the clusterng result. Perceptual Feature Extracton After the processng of pre-segmentaton, proper features should be extracted to represent the mage at a vsual percepton level. Here, lower mage features serve as the descrpton of the vsual percepton level. In the nterest of followng processng, such as object recognton, the mage features should be selected carefully. In the case of landscape mages, we select color and texture features to represent the extracted meanngful regons. In the vsual percepton layer, perceptual features are to be extracted. Both color feature and texture feature are used here, as people are more senstve to color and texture nformaton n a natural mage. To represent the color nformaton of mages, a color feature set, whch s a sub-space of the color space, s ntroduced to descrbe the mage content n terms of human vsual percepton. Because such vsual nformaton representaton deals wth both hue and ntensty, t s named Mxed Color Feature (MCF). In practce, based on MCF, a hstogram of mages s used to represent color vsual nformaton (Gao, 000a). A proper dstance measurement named Weghted Nearest Matchng s also ntroduced n related processng, such as feature comparson. Texture features based on wavelet are used snce mult-resoluton representatons are wdely employed n texture analyss for the sake of strong arguments found n psycho- Fgure 3: Example of Non-Parametrc Clusterng (a) (b) (c) (d)

11 64 Zhang, Gao, & Luo vsual research. A typcal herarchcal wavelet transform decomposes sgnals nto a set of sub-mages n the lower frequency band, whch s enough n generc sgnal analyss. However, n the case of texture mages, whch possess a consderable proporton of energy n the ntermedate frequency band, the hgher frequency band should be further decomposed to capture more nformaton. The wavelet package transform decomposes sgnals not only n the lower frequency band but also n the hgher frequency band. In ths secton, a new texture descrptor s proposed. Frst, a Daubeches wavelet package transform s appled to the orgnal mage and as a result 8 sub-bands are generated. Then, for each sub-band, the average and standard varance of wavelet-coeffcents are extracted as texture feature vectors. Color Informaton Representaton In the HSI color model, color nformaton s represented by three values: hue (H), saturaton (S) and ntensty (I). Among the three values, H s the closest to human vson and plays the key role n determnng the color vson. Therefore, the value of H s often extracted as the color feature to reduce the dmenson of feature space whle reservng the color nformaton of the orgnal mage as accurately as possble. Nevertheless, the color nformaton unquely represented by H sometmes performs badly, for example, n the case of S = 0, the value of H has no defnton. In other words, such a defnton of H could not nclude all the factors contrbutng to human vsual percepton. Here, another method to construct the color feature s presented. For the convenence of nterpretaton, a varable s ntroduced as follows: Balance max( R, G, B) 00% L Max (0) where L max represents the maxmal value of each color channel. The vsual nformaton can be dvded nto two parts, hue nformaton and ntensty nformaton, n terms of the followng crterons:. If the value of S s consderably small, the value of H could be neglected and only the ntensty nformaton should be consdered.. If the value of Balance s consderably small, the vsual nformaton could also be descrbed only by ntensty nformaton. In ths way, the vsual nformaton could be descrbed by a combned vector V: V { H, H,, H N, I, I,, I H NI } () where N H represents the dmenson of hue part whle N I represents that of ntensty part so that the total dmenson of V s N H + N I. It s evdent that f H and I are orthogonal, the redundancy wll be reduced to the mnmal level (Zhang, 998). So, a transformaton n HSI space s carred out to make H and I to be easly separable and make the H dstrbuton more unform (Lu, 998).

12 Object-Based Technques for Image Retreval 65 Dstance Measurement Snce n MCF the H and I ndcate dfferent color nformaton, the dstance between two ponts n ths sub-space could not be smply measured by the geometrc dstance. A segmental dstance measurement s requred. For the convenence of nterpretaton, the value of H, =,, N H s scaled from [0, 360 ] to [0, N H -]. Smlarly, the value of I, =,, N I s scaled from the regon [0, L Max ] to [N H, N H + N I ]. Suppose there are two ponts V and V j among the vector V, the normalzed dstance between V and V j could be defned as Ds V, V ) : MCF ( j. If V V [ H, H,, H ],, j NH Ds MCF ( V, V j V V j N H ) N H V V N H j V V j V V j N N H H (). If V V [ I, I,, I ],, j N I Ds V V j MCF ( V, V j ) (3) N I 3. If V [ H, H, H N ], V j [ I, I,, I N ] H I or V I, I,, I ], V [ H, H,, H ], Ds [ NI j NH MCF ( V, V ) j (4) The above three equatons defne the normalzed dstance among the sub-space MCF. Wavelet Package Texture Feature Mult-resoluton representatons are wdely used n texture analyss for the sake of strong arguments found n psycho-vsual research. A typcal herarchcal wavelet transform decomposes sgnals nto a set of sub-mages n the lower frequency band, whch s enough n generc sgnal analyss. However, n the case of texture mages, whch possess a consderable proporton of energy n the ntermedate frequency band, the hgher frequency band should be further decomposed to capture more nformaton. In the case of texture mages, whch possess a consderable proporton of energy n the ntermedate frequency band, the wavelet package transform that decomposes sgnals not only n the lower frequency band but also n the hgher frequency band could be used to decompose the hgher frequency band to capture more nformaton (Cofman, 99). Based on the above dscusson, the followng texture descrptor s proposed. Frst, a Daubeches wavelet package transform s appled to the orgnal mage and as a result

13 66 Zhang, Gao, & Luo 8 sub-bands are generated. Then, for each sub-band, the average and standard varance of wavelet coeffcents are extracted as texture feature vectors. Gven the output of k-th sub-band, x k (, j), j M k and the sze of the sub-band, M k M k, k =,,, 8, the average and standard varance could be computed by (usng L -norm): x k M k x k M k M k k, j x (, j) (5) k M k k M k, j x (, j) x k (6) Regon Descrptor To descrbe each meanngful regon completely, some regon descrptors, ncludng regonal area, centrod, and form factor, are also employed. Ther defntons are gven below. Let I represents the whole mage, R represents meanngful regons of I, the normalzed area of regon R s: A R ( x, y) R ( x, y) I (7) The centrod coordnates of regon R are: x X A y Y A R ( x, y) R x y R ( x, y) R (8) where X and Y represent the extents along drecton x and y of the whole mage, respectvely. The form factor of regon R s: F P 4S (9) where P represents the permeter of the regon R whle S represents the area of the regon R. Although color, texture and regon features used here are low-level mage features, these features are appled here on extracted meanngful regons and amed at representng the outstandng character of the meanngful regon but abandonng others trval vsual

14 Object-Based Technques for Image Retreval 67 percepton. Ths s n contrast to tradtonal processes that apply feature computaton on the whole mage. Object Recognton Of the whole object layer, the key s to recognze what a meanngful regon represents n terms of human bengs knowledge. Automatc object recognton overcomes the dsadvantage of large overhead n manual labelng whle t throws off the nsuffcent mage content descrpton by lower mage features. In the followng, an teratve procedure s used to approach the correct recognton result. The object recognton s bascally a tranng-and-testng procedure. That s, the system would gve ts recognton result n terms of the knowledge havng been stored n t. To make the problem smpler, t s assumed that there are fnte types of nterestng objects n a gven mage database. In fact, ths requrement could often be satsfed n practce as only lmted mage contents are consdered n one applcaton. The object recognton s performed n an teratve way. Context-based knowledge would be obtaned durng ths process, helpng to reach the correct recognton result. There are three major steps ncluded n ths procedure: dynamc weght determnaton, multple processes to recognton and mult-condtonal judgng. Expermental results n the context of content-based mage retreval have shown the advantage of ths method, compared wth tradtonal methods for object recognton. Flowchart for Object Recognton The flowchart for teratve recognton of objects based on extracted meanngful regons s shown n Fgure 4. In Fgure 4, the object recognton s consdered as a tranngand-testng procedure. That s, the system would gve ts recognton result n terms of the knowledge havng been stored n t. To make the problem smpler, t s assumed that there are fnte types of nterestng objects n a gven mage database. In fact, ths requrement could often be satsfed n practce as only lmted mage contents are consdered. Durng the teratve procedure, context-based knowledge would be obtaned and helped to reach the correct recognton result. The two major steps ncluded n ths procedure: dynamc weght determnaton and multple processes to recognton, wll be dscussed brefly n the followng. Dynamc Weght Determnaton To capture dfferent aspects of mages, multple features are often used. How to determne the proporton of each feature n a descrpton would heavly affect the performance of retreval. The method proposed here s to count the dstance between the feature obtaned from an extracted meanngful regon n the current mage (F m ) and the -th sample n the tranng feature set {F t (), =,,, N}. In practce, the mean and standard varance of these dstances are frst computed as: N Ds[ Fm, Ft ( )] N (0) N Ds[ Fm, Ft ( )] N ()

15 68 Zhang, Gao, & Luo Fgure 4: Procedure for Object Recognton Tranng Set Extracted Regon Dynamc Weght Determnaton Weghted Feature Multple Recognton Context-based Knowjedge Combned Results Then, the weght for feature j s determned by: Weght () In Equaton (), the weght s nversely proportonal to the mean of the dstance n order to normalze dstance values from varous mage features. The weght s proportonal to the varance, whch ndcates the dsperson of dstance n the feature space between the meanngful regon and each sample n the tranng set. In fact, the small varance ndcates that ths knd of feature plays a trval role n object recognton. The large varance would heavly nfluence the result of recognton because the meanngful regon s closer to the tranng set n the feature space. It could be seen from Equaton () that the weght for each feature s not pre-determned n advance, but s determned dynamcally. Multple Recognton Due to the lghtng condtons and/or varety of object appearance, the objects belongng to the same category can have dfferent vsual aspects. To recognze a partcular object and the object set n an mage correctly, two recognton processes are performed here. The frst one s, wth respect to each meanngful regon extracted from the mage, to calculate the dstances between the consdered regon and all object set J n the tranng set as descrbed above. These dstances are then ranked n an ascendng order: Ds[F m, F t ()] < Ds[F m, F t ()] << Ds[F m, F t (N)]. To capture the most sgnfcant objects n the tranng set whle dscardng trval ones, only the frst N s dstances are consdered n recognton:

16 Object-Based Technques for Image Retreval 69 R N s E j ( k) Arg max jj k Ds( Fm, Ft ( k)) (3) where E j s defned as:, E j ( k) 0, f F ( k) otherwse t j (4) The second one s to consder all the objects n the tranng set together to calculate the feature dstance to fnd the one that corresponds to mnmal dstance. In fact, the process s an nstance of classfcaton usng mnmal dstance crteron. R Arg mn Ds[ F, F ( k)] k N tm t (5) Based on the above two recognton crtera, a unque recognton result s generated by combnaton. Ths process can be terated untl the fnal recognton result s acceptable (n terms of the mage composton, accordng to some a pror knowledge). In ths process, some new knowledge, probably to be used n the next teraton, can be acqured and ntegrated to generate a current recognton result. Wth ths teratve procedure, the context-based knowledge s gradually mproved and the correct recognton result s gradually approached. Object Descrpton and Matchng Object Descrpton To descrbe objects n an mage, a matrx T s defned. It s a dagonal matrx wth each entry ndcatng the attrbute of every meanngful regon n the mage. Let S be the set of all types of objects (totally M) n the mage database, and s be the -th object n S, then T could be expressed as: T Dag [ t, t,, t M ] (6) where: t 0 f s S, otherwse,, M (7) In addton, a relaton matrx R s s defned. It s a K K matrx to ndcate the spatal relatonshp between every two meanngful regons, wth K representng the number of meanngful regons n the whole mage. For nstance, the spatal relatonshp between the

17 70 Zhang, Gao, & Luo -th regon and the j-th regon could be represented by settng the value of r j n R s, whch s located n the -th lne and the j-th column. Let T r, T r,..., T rn be the content representaton matrx of each relevant mage, where subscrpt r ndcates the set of relevant mages and N s the number of relevant mages. Then a correlaton matrx C could be defned as follows: C Dag[ c, c,, c M ] T N r t t t M t t t M t N t N t M N (8) Fnally, the decson functon B(N ) elements n the matrx: could be defned as the sum of the dagonal B( N ) tr[ C] (9) It could be seen from Equatons (6)~(9) that the decson functon reflects whether all the relevant mages have common contents, that s, the same objects. The system would present dfferent queryng crtera n terms of B(N). Object Matchng The object matchng procedure s shown n Fgure 5 (Zhang, 00). The case of B(N) = 0 ndcates that the relevant mages do not have common objects. The case of B(N) > 0 ndcates that the relevant mages do have common objects. In the former case, feature based matchng wll be performed, whle n the latter case, the matchng wll be based on objects as descrbed n the followng. For object matchng, the system would extract smlarty nformaton from the common objects n all relevant mages to perform the match processng between the relevant mages and canddate mages. Let F = {f =,,, L} be a set of representaton for both the vsual percepton level and the object level, such as color, texture and spatal relatonshp. The weghts n terms of the smlarty nformaton of all the relevant mages should be updated as follows: N ~ d N ( N ) N jk j d ( f rj, f rk ),, L (30) ( ) N N N N j k j d ( f rj, f rk ~ ) d,,, L (3)

18 Object-Based Technques for Image Retreval 7 Fgure 5: Procedure of Self-Adaptve Relevance Feedback Relevant Images Object Matchng N B(n) < 0 Y Feature Matchng Refned Results ~ d W,,, L (3) In Equaton (30), the symbol r n the subscrpt of f and f means that ths tem s from the set of relevant mages. Smlarly, n the followng, q would be used to ndcate an mage from the database. Unlke the general standard varaton based on L norm, Equaton (3) s based on L norm, whch has the advantage of smple calculaton and shows good performance n practce. Equaton (3) shows that the value of W s proportonal to d ~ but nversely proportonal to. Usng as the denomnator of W s based on such a hypothess that the larger s, the less f could represent the smlarty nformaton of the relevant mages. The goal of selectng d ~ as the numerator of W s to normalze the value of the dstance so that dfferent representatons could be put together. Further nterpretaton about t would be gven n the followng. What should be stressed s that the above processng should be carred out wth each common object of the relevant mages. That s, there are dfferent weghts for all common objects of the relevant mages. Based on the above statements, the queryng crteron for common nformaton matchng could be proposed wth the smlarty functon. Let C be the correlaton matrx for the relevant mages and T q be the content representaton matrx of the -th mage n the database. Then, a new decson functon B CIM (q) could be ntroduced as: rj rk B CIM ( q) tr( C q c ) tr q c q tr( CTq ) M cq (33) where C q represents the correlaton matrx of the -th mage n the database. The proper canddate from the database should satsfy B CIM (q) B(N). Then, to each proper canddate, ts smlarty functon could be calculated by:

19 7 Zhang, Gao, & Luo S( q) N N L l l l l Wk Dk wk d ( f q, k, f rj, k ) (34) j k M, c k q j k M, c k l q Gong through the whole database by the proposed method for common nformaton matchng, each proper canddate would get ts value of smlarty, whch could be used to order the returned queryng results. Experments and Dscussons To test the performance of the proposed methods, a prototype retreval system has been mplemented based on the above-descrbed technques. The whole system could be dvded nto fve modules, such as user nput, system output, mage database, queryng mechansm and feedback mechansm (Gao, 00). Each of them has ts own functon whle connectng wth each other to form an ntegrated system. Usng ths prototype system, the followng three experments have been conducted to verfy the performance and effectveness of the above presented technques. Object Recognton Here we select landscape mages as the data source. Seven object categores often found n landscape mages are selected: mountan, tree, ground, water, sky, buldng and flower. There are two reasons for ths selecton. Frst, landscape mages generally nclude complex color and texture nformaton, and they are not easy to be treated by smple recognton procedures. Second, n landscape mages, there are many objects havng varable appearances under the same defnton of objects. For nstance, under the category sky, there exst dfferent appearances such as clear sky, sunny sky, cloudy sky and so on. Instead of vsual features, object recognton s mandatory n ths case. In Fgure 6, four examples of test results about the object recognton are presented. The frst row s for orgnal mages. The second row gves the extracted meanngful regons. As s mentoned above, the procedure of meanngful regon extracton ams not at precse segmentng but rather at extractng major regons, whch are strkng to human vson. The thrd row represents the result of object recognton, wth each meanngful regon labeled by dfferent colors (see the fourth row) to ndcate the object category t belongs to. Image Retreval Based on Object Matchng Based on object recognton, the retreval can be conducted n the object level. One example s shown n Fgure 7. The user has submtted a query n whch three objects mountan, tree and sky are selected and thus, requred. Based on ths nformaton, the system searches n the database and looks for mages wth these three objects, the returned mages from the mage database are dsplayed n Fgure 7. Though the mages n Fgure 7 are dfferent n the vsual percepton sense as they may have dfferent colors or shapes or structure appearances, however, all these mages have the requred three objects. In Fgure 7, the orderng of these returned mages s based on object smlarty, whch s provded by vsual percepton descrptors. One descrptor that can be used s the area of requred object, as n the larger the object n the mage, the more nterest to the user.

20 Object-Based Technques for Image Retreval 73 Fgure 6: Expermental Results for Pre-Segmentaton and Object Recognton Mountan Tree Ground Water Sky Buldng Flower If the user selected the k-th object, then the orderng of returned mages (have the requred object) s made accordng to the normalzed area of the k-th object (denoted by A k ) n these mages. When the user selected totally K objects, then the orderng of returned mages (have all requred objects) wll be made accordng to: S K A k k (35) Fgure 7: Expermental Results Obtaned by Oject Matchng

21 74 Zhang, Gao, & Luo Fgure 8: Further Results Obtaned by Object Relatonshp Matchng Based on object recognton, the retreval can be further conducted usng object relatonshp. One example s gven n Fgure 8, whch s a result of an advanced search. Suppose the user made a further requrement based on the queryng results of Fgure 7, n whch the spatal relatonshp between objects mountan and tree should also fulfll a left-to-rght relatonshp. In other words, not only the objects mountan and tree should be presented n the returned mages (the presentaton of sky objects s mplct), but also the mountan should be presented n the mages as the left sde of the tree (n ths example, the poston of sky s not lmted). The results shown n Fgure 8 are just a sub-set of Fgure 7. Self-Adaptve Relevance Feedback In the current content-based mage retreval system, relevance feedback has been often ntegrated nto the system to grasp the descrpton of mages and refne the query results. However, the relevance feedback relyng on the low-level features tself could not completely catch mage semantcs and thus, the performance of the system s heavly affected by the dffculty to meet users requrements. Based on the mult-level descrpton model, the scheme of self-adaptve relevance feedback could be establshed. The man dea of the proposed scheme s to analyze the fed-back relevant mages marked by the user n dfferent levels to reach comprehensve smlarty analyss. Then, a self-adaptve procedure s carred out to refne the query, by whch dfferent queryng schemes could be used n terms of dfferent results of smlarty analyss. The fnal goal of the scheme s to make the queryng results meet the user s demand as closely as possble. The whole procedure wll be carred out n two steps. Frst, smlarty analyss s carred out on dfferent levels of descrpton along the relevant mages to extract the relevant nformaton. Then, dfferent queryng schemes would be used to search optmally matched mages n terms of dfferent results of smlarty analyss. To ths purpose, a decson functon s calculated, whch s based on the fed-back nformaton obtaned from the object layer. Then, one of the two matchng processes, common nformaton matchng or respectve nformaton matchng (see below), would be carred out accordng to the value of the decson functon. In addton, the procedure of relevance feedback can be carred out wth memory or wthout memory, whch makes the feedback mechansm more flexble and convenent. In case of the feedback wthout memory, each feedback s an ndependent procedure, n whch all of the relevant mages selected n prevous teratons would be gnored. In case of the feedback wth memory,

22 Object-Based Technques for Image Retreval 75 Fgure 9: Retreval wth Respectve Informaton Matchng (a) (b) (c) the relevant mages selected n prevous teratons would be taken nto account n the current teraton. A tme delay curve has been proposed to smulate human bengs memory mechansm n feedback wth memory (Gao, 00). In the followng, two expermental examples are provded to show the results of relevant feedback based on object levels. Fgure 9 presents a typcal nstance that there s no common object of the relevant mages. Fgure 9(a) shows the sample mages provded by the system. Here the No. 4 n the frst row and No. 4 n the second row are relevant mages selected. It could be easly seen that these two mages have no common objects, leadng to the respectve nformaton matchng. The query result s shown n Fgure 9(b). It should be notced that snce t s the frst tme of feedback, there s no dfference usng feedback wth memory or wthout memory. From Fgure 9(b), t could be seen that returned mages nclude varous

23 76 Zhang, Gao, & Luo Fgure 0: Retreval wth Common Informaton Matchng (a) (b) (c) objects but they show some common characters. For example, they all somewhat show the color of yellow. However, the user changes hs nterest to the two mages No. and No.5 n the second row that show the common object of buldng. Then, the feedback s carred out wthout memory and the result s gven n Fgure 9(c). Fgure 0 shows another nstance of feedback but wth common nformaton matchng. Fgure 0(a) s the result of a frst round retreval, n whch the object flower s requred. The three selected mages (No. 5 n the frst row as well as No. 3 and No. 5 n the second row) show the same character of large format and the common nformaton s utlzed to fnd the optmally matchng mages. The returned result s shown n Fgure 0(b). To obtan a more satsfyng query result, two more mages are selected (No. 5 n the frst row and No. 5 n the second row) n Fgure 0(b). The feedback wth memory s carred out n ths stage, reachng at the query result shown n Fgure 0(c). It could be seen that the query result s gradually refned wth the teratve feedback. DIRECTION OF FUTURE RESEARCH The procedure and technques presented n the above could be further mproved and pursued n a number of promsng drectons:. The extracton of a meanngful regon that s at the ntermedate level between the orgnal mage and the nterestng object of an mage has played an mportant role for effectve vsual level process. Experments have shown that successful extracton of meanngful regons from stll mages and vdeo shots helps to ntegrate the unsupervsed and adaptve clusterng technques n CBIR (content-based mage retreval) and CBVR (content-based vdeo retreval) systems (Luo, 00). However, the robustness of extracton, especally wth complcated mages, needs to be

24 Object-Based Technques for Image Retreval 77 amelorated. For ths purpose, more characterstcs of mages should be further consdered.. The proposed object-based framework for effcent mage representaton has enabled flexble access and manpulaton of mage content. However, n the object layer, the semantc object delneaton for general sources remans a challengng task. As mage segmentaton s not just a task that could be worked out at a low level, some hgh level knowledge should be ncorporated (Zhang, 00). In addton, as human bengs are stll far from knowng all the cogntve detals from the real world, how to automatcally form semantc objects s perplexng. How to effcently descrbe semantc objects as congruous to humans senses as possble should be studed. 3. The mult-layer descrpton model presented n Fgure only shows four layers. In fact, on the top of object layer, a scene layer that should be the hghest layer n the proposed model should be consdered. The descrpton for a scene layer should be provded by the scene understandng, whch s related to human knowledge for mage contents, lke the object recognton n the object layer. The process of scene understandng could be a tranng procedure, and the knowledge database for scene understandng could be updated durng queryng, n terms of the users comprehenson. 4. Relevance feedback s useful to ncorporate human ntenton and to approach the optmal retreval results. However, feedback s just a method for refnng the results so that t could not totally determne the performance of mage retreval systems, and how to make ths process fast followng the users aspraton n the course of retreval s nterestng. When the system s based on lower level features, relevance feedback could only mprove the retreval results to some degree. The use of relevance feedback based on hgh-level content descrpton n the object level could further mprove the performance. On the other sde, a potental drecton would be to use assocaton feedback based on feature elements (Xu, 00). CONCLUSION In summary, ths chapter proposed an object-based scheme for mage retreval and descrbed the technques to fulfll ths task n detal (wth prncple descrpton, algorthm mplementaton, and retreval expermentaton and result dscussons). On the bass of a mult-layer descrpton model, a progressve procedure for mage content understandng s presented. Startng from meanngful regon extracton, some perceptual features are selected from extracted regons for the further recognton of correspondng objects. In the object level, the content descrpton and object matchng are carred out to effcently retreve expected mages, and wth self-adaptve relevance feedback, the results are refned accordng to users wshes n an explct manner. A prototype system based on these technques has been mplemented and real retreval experments have been conducted, the functonalty and performance of the system has been verfed. Further research drectons on mprovng the robustness of meanngful regon extracton, on descrbng objects wth more semantc meanng, on performng scene level process to fully understand the contents of mages and on usng feedback more effcently are ndcated.

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