SRBIR: Semantic Region Based Image Retrieval by Extracting the Dominant Region and Semantic Learning

Transcription

1 Journal of Computer Scence 7 (3): , 2011 ISSN Scence Publcatons SRBIR: Semantc Regon Based Image Retreval by Extractng the Domnant Regon and Semantc Learnng 1 I. Felc Raam and 2 S. Vall 1 Department of MCA, St. Joseph s College of Engneerng, Jeppaar Nagar, Old Mamallapuram Road, Chenna , Inda 2 Department of CSE, College of Engneerng, Gundy, Anna Unversty Chenna, Sardar Patel Road, Gundy, Chenna , Taml Nadu, Inda Abstract: Problem statement: The Semantc Regon Based Image Retreval (SRBIR) system that automatcally segments the domnant foreground regon, consstng of the semantc concept of the mage, such as elephants, roses and does the semantc learnng, s proposed. Approach: The system segments an mage nto dfferent regons and fnds the domnant foreground regon n t, whch s the semantc concept of that mage. Then t extracts the low-level features of that domnant foreground regon. The Support Vector Machne-Bnary Decson Tree (SVM-BDT) s used for semantc learnng and t fnds the semantc category of an mage. The low level features of the domnant regon of each category mage are used to fnd the semantc template of that category. The SVM-BDT s constructed wth the help of these semantc templates. The hgh level concept of the query mage s obtaned usng ths SVM-BDT. Smlarty matchng s done between the query mage and the set of mages belongng to the semantc category of the query mage and the top mages wth least dstances are retreved. Results: Experments were conducted usng the COREL dataset consstng of 10,000 mages and ts subset wth 1000 mages of 10 dfferent semantc categores. The obtaned results demonstrate the effectveness of the proposed framework, compared to those of the commonly used regon based mage retreval approaches. Concluson: Effcent mage searchng, browsng and retreval are requred by users from varous domans, such as medcne, fashon, archtecture, tranng and teachng. The proposed SRBIR system ams at retrevng mages based on ther semantc content by extractng the domnant foreground regon n the mage and learnng ts semantc concept wth the help of the SVM-BDT. The proposed SRBIR system provdes an effcent mage search based on semantcs, wth hgh accuracy and less access tme. Key words: Semantc regon, semantc template, Support Vector Machne (SVM), Bnary Decson Tree (BDT), regon based mage retreval, statstcal smlarty, Database (DB), semantc learnng, query mage, foreground regon, Artfcal Neural Networks (ANN) INTRODUCTION Conventonal content-based mage retreval systems use low level features, such as color, texture and shape. But there s a semantc gap between the low level mage features and the hgh level semantcs perceved by the user. Therefore, to mprove the retreval accuracy, a CBIR system should reduce ths semantc gap. There are several technques avalable to reduce the semantc gap. Machne learnng technques can be used to assocate the low level features wth the hgh level semantcs. The supervsed learnng technque, SVM s used to solve multclass problems. In the mult class SVM, the SVM s traned usng the DB mages of known categores and the class label s predcted for the query mage (Rahman et al., 2007). Then the query mage s tested only aganst those class mages. Yogameena et al. (2010) have used the SVM to detect an ndvdual s abnormal behavor n a human crowd. The SVM has acheved great success n pattern recognton and t can be appled n many domans, such as hand-wrtng recognton, face recognton, voce recognton, text classfcaton and mage processng (Yogameena et al., 2010). Rahman et al., (2007) uses the relevance feedback mechansm to learn the users ntenton. Obect ontology can be used to defne hgh level semantcs (Mezars et al., 2003; Lu et al., 2007). In ths system, each regon of an mage s Correspondng Author: I. Felc Raam, Department of M.C.A, St. Joseph s College of Engneerng, Jeppaar Nagar, Old Mamallapuram Road, Chenna , Inda 400

2 descrbed by ts average color, ts poston, ts sze and shape. Obect ontology provdes a qualtatve defnton of the hgh level query concepts (Lu et al., 2007). Semantc templates can be generated to support hgh level mage retreval. The Semantc template s a map between the hgh level concept and the low level vsual features. The Semantc template s the representatve feature of a concept calculated from a collecton of sample mages (Zhang and Lu, 2008; Lu et al., 2007). Many systems explot one or more of the above technques to mplement hgh-level semantcbased mage retreval. Among the above mentoned technques, machne learnng tools, such as the Support Vector Machne (SVM), Artfcal Neural Networks (ANN) and Bayesan Networks (BN) are often used for semantc learnng. The proposed content based mage retreval framework, whch combnes the SVM-PWC and the Fuzzy C-Mean clusterng to provde an effcent retreval and also reduces the search space and tme. Decson Tree (DT) learnng, such as the ID3, C4.5 and CART are also used n data classfcaton. The DT learnng method s very useful n mage semantc learnng. Zhang and Lu (2008) proposed a regon based mage retreval system whch makes use of decson tree learnng. It frst selects the regon of nterest from the mage. Then, that regon s low level features are used to fnd the semantc concept of the mage (Zhang and Lu, 2008). Ther decson tree learnng method s compared wth the proposed method and the latter s seen to perform comparatvely well. The SVM-BDT s an effcent classfcaton technque, whch combnes the essental features of the SVM and the hgh accuracy of the bnary decson tree (Madzarov et al., 2009). Madzarov and Gorgevk (2009) used the bnary decson tree mechansm for hand wrtten letters and dgts recognton. Mao et al. (2005) have used the fuzzy support vector machne and the bnary decson tree for multclass cancer classfcaton. It produces good results n fndng the most mportant genes that affect certan types of cancer wth hgh recognton accuracy. So ths SVM-Bnary decson tree technque s used n the SRBIR. The SRBIR system s a regon based mage retreval system usng the SVM-BDT to extract the hgh level mage semantcs. There are several methods n segmentng the regons from the mage. Suhasn et al. (2008) used a graph based segmentaton for segmentng the regon from an mage. Zhang and Lu, (2008), developed a regon based mage retreval system. Ths regon based mage retreval system selects the regon of nterest n the mage and extracts the features of that regon and compares them wth the features of the regons extracted from the Database (DB) mages. 401 Selectng the regon of nterest for each of the DB mages requres much tme for tranng. So, n ths mplemented SRBIR system, the automatc segmentaton of the domnant regon n the mage s proposed. Ths automatc segmentaton provdes the domnant foreground regon n the mage, whch manly consttutes the semantcs of the mage. Ths segmentaton algorthm provdes the sold regon and not the outlne alone. Therefore, the extracted domnant foreground regon would be less dstorted and possess less nose. Hence, the low level features of the mages are mantaned n the regon wthout much dstorton. The mplemented SRBIR system uses the SVM- BDT for semantc learnng. The color and texture features are extracted from the domnant regon of each of the DB mages. The color, texture and color-texture semantc templates are found for each semantc category. These semantc templates are used n constructng the SVM-BDT. The expermental results show that our approach provdes hgher accuracy than other decson tree learnng methods lke the DT-ST, ID3 and C4.5. The remander of ths study s organzed as, system descrpton, automatc segmentaton of the domnant foreground regon, feature extracton, semantc learnng based on the SVM-BDT, expermental results and dscusson. Fnally, the concluson and future work are provded. MATERIALS AND METHODS System descrpton: The system fnds the domnant foreground regon n each of the database mages and extracts the low level color and texture features for the domnant regon of each mage. These color texture features are stored n a database. For each category of mages, the system fnds the semantc templates, namely the color template, texture template and color texture template. These templates are used n buldng the SVM-Bnary Decson Tree (SVM-BDT) whch s used to fnd the class label of the query mage. Durng retreval, the proposed system fnds the domnant foreground regon of the query mage and fnds ts color and texture features. These features are used as nput to the SVM-BDT and t predcts the label of the query mage. The dstance between the color texture features of the query mage and the color-texture features of the domnant regon of the DB mages of the predcted class s found. The dstance measures used n ths mplemented system are the Eucldean dstance, Bhattacharya dstance and Mahalanobs dstance. These dstance values are sorted n the ascendng order and

3 Fg. 1 Block dagram of the SRBIR the top k mages correspondng to the lowest dstance are dsplayed. Fgure 1 s the block dagram of the proposed system, SRBIR. Segmentaton of the domnant foreground regon: The domnant foreground regon of an mage s the regon whch occupes most of the space n the mage foreground. The SRBIR system extracts the domnant foreground regon whch gves the semantcs of the mage. Also, the obtaned domnant regon s a sold regon and not the outlne. Thus, the domnant regon extracted, has reduced nose. So the low-level features extracted from the domnant regon wll not have more dstortons. Algorthm for extractng the domnant foreground regon of an mage: 1. An RGB mage s read and the ndexed mage s obtaned from t. The ndexed mage s used to get back the color from the correspondng gray scale mage Let nd_mg Indexed mage 2. The gray scale mage s obtaned from the color mage 3. Nose s removed by applyng medan flterng 4. The edges of the mage are found by usng canny edge detecton. Heath et al. (1997) says that the 402 Canny edge detecton algorthm can produce better edge mages, f care s taken n adustng the parameters manually. So, the canny edge detecton algorthm s used here for detectng the edges of the mage. 5. Smoothng of the mage s done to reduce the number of connected components 6. The connected components of the mage are found by usng the 8 connectvty L matrx contanng the component numbers 7. The component number for the background mage s 0. Among all the connected components excludng the background component, the bggest connected component n the mage s found max_nd Index of the bggest connected component 8. For the pxels that are n the maxmum connected component, the orgnal pxel value from the ndexed mage s coped and for all the remanng pxels the value s set to zero. Ths bggest connected component s treated as the domnant regon Let [mx, my] sze of the mage for =1 to mx { for =1 to my { f (L(,)== max_nd) copy n(,) nd_mg(,)

4 else set n(,) 0 9. The domnant regon obtaned s not a sold regon To make t a sold regon for =1 to mx { for =1 to my { f ((n(,)== 0) and((n(,-1)!=0)or n(-1,)!=0)) { set flag 0 f (n(-1,)!=0) for z=+1 to mx f (n(z, )!= 0) set flag 1 f (n(, -1)!=0) for z=+1 to my f (n(, z)!= 0) set flag 1 f (flag == 1) n(, ) nd_mg(,) 10. Now the sold regon s converted back nto a color mage, usng the color mappng. Fgure 2 s the result of the automatc segmentaton of the domnant regon from an mage. The frst column mages 2a, 2c, 2e, 2g and 2 are the orgnal mages and the correspondng second column mages 2b, 2d, 2f, 2h and 2 are the domnant regons of those mages, obtaned by the automatc segmentaton of the domnant regon. The mages 2b, 2d, 2f and 2h show perfect segmentaton; Fg. 2 s wth some nose. Many of the regon based mage retreval systems are based on the selecton of the regon of nterest. In the regon based mage retreval proposed by Zhang and Lu, (2008), the regon of nterest s selected by the user and the regon n that porton s extracted. The features of ths regon are consdered for smlarty matchng. In the SRBIR system, the automatc segmentaton of the domnant foreground regon s attempted and the result s gven n Fg. 2. Also, the segmentaton based on the user s selecton of the regon of nterest has been attempted n ths study. Fgure 3 shows the segmentaton based on user selecton. Fgure 3a and 3c are the orgnal mages and Fg. 3b-3d are the segmented regons. The segmentaton by selectng the regon of nterest has been carred out usng the SegTool. channel are used n representng the color feature vector n the Hue, Saturaton and Value n the HSV color space. The sx dmensonal ( μh, μs, μv, σh, σs, σv) color feature vector (f c ) s extracted. μ represents the mean and σ corresponds to the standard devaton of each color channel n the HSV (Rahman et al., 2007; Rahman et al., 2005). The texture nformaton s extracted from the graylevel co-occurrence matrx. A gray level co-occurrence matrx s defned as the sample of the ont probablty densty of the gray levels of two pxels separated by a gven dsplacement d and angle θ (Rahman et al., 2007; Lu et al., 2005). The four co-occurrence matrces of the four dfferent orentatons (horzontal 0, vertcal 90 and two dagonals 45 and 135 ) are constructed. The co-occurrence matrx reveals certan propertes about the spatal dstrbuton of the gray levels n the mage. Hgher order features such as energy, contrast, homogenety, correlaton and entropy are measured usng Eq. 1-5 on each gray level co-occurrence matrx (Abbad et al., 2010; Rahman et al., 2007; Haralck et al., 1973) to form a fve dmensonal feature vector: 2 Energy = p (, ) (1) Feature extracton: For ether of the segmentaton methods, whether t s auto segmentaton or user guded segmentaton, the color and texture features are obtaned for the domnant regon of the mage. The frst, second and thrd central moments of each color and dshes. The nput to the SVM-BDT s the low level 403 Entropy = p (, )log p(, ) (2) 2 Contrast = ( ) p(, ) (3) p(, ) Homogenety = (4) 1+ Correlaton = ( μ)( μ)p(, ) σσ (5) Fnally, a twenty dmensonal feature vector (f t ) s obtaned by concatenatng the feature vectors of each co-occurrence matrx. So, the color-texture feature vector of dmenson 26 s obtaned (f ct = f c + f t ). Learnng the mage semantcs usng the SVM-BDT: The purpose of constructng the SVM-BDT s to assocate the low-level features of the mage regons wth the hgh-level concepts. Fgure 4 s the block dagram of the constructon of the SVM-BDT. The COREL dataset consstng of 1000 mages of 10 dfferent categores s used. The dfferent categores are mages of Afrcan faces, beaches, buldngs, buses, dnosaurs, elephants, roses, horses, snowy mountans

5 (a) (b) (a) (b) (c) (d) (c) (e) (d) (f) Fg. 3: Image and the segmented regon usng the SegTool the low-level features of all the sample regons. For the th sample regon n class, where ( = 1, 2,, 100 ) and ( = 1, 2,, 10), ts color and texture features are gven by ( μ, μ, μ, σ, σ, σ ) and (t 1, t 2,, t 20 ) h s v h s v respectvely. For the frst dmenson of the color and texture features, the centrod (Zhang and Lu, 2008) s calculated usng Eq. 6-7: 1 μ h = = μ 1 h (6) t1 = t = 1 1 (7) 100 (g) () Fg. 2: Image and domnant foreground regon usng the proposed algorthm regon features and the output s any one of the 10 semantc concepts. Semantc template constructon: Zhang and Lu, (2008) defnes a Semantc template as the centrod of (h) () Hence the color and texture template of concept s C = ( μh, μs, μv, σh, σs, σ v,) and T = (t 1,t 2,...t 20,). The color-texture template s calculated as CT = C + T. Constructon of the SVM-BDT: The SVM bnary decson tree constructon conssts of two maor steps. The frst step nvolves constructng the Bnary Decson Tree (BDT) by clusterng the varous classes of the DB mages. The second step nvolves assocatng a bnary class SVM at each node of the BDT obtaned n the frst step (Lu et al., 2007). We use ths approach n our regon based mage retreval framework. If K s the number of classes, then, the Eucldean dstance between the color-texture templates of all the K classes s found. Thus, the KXK dstance matrx s obtaned. The dstance matrx s used for further groupng. The two classes that have the largest Eucldean dstance between them are assgned to each of the two clusterng groups and the color-texture template of the two classes s taken as the cluster center for the correspondng group. 404

6 Fg. 4: Block dagram of the constructon of the SVM- BDT After ths, the par of classes each of whch s closest to the cluster centers of the groups s found and assgned to the correspondng group. Now, the cluster center s updated to the color-texture template of the class that has been ncluded recently n the group. The process contnues by fndng the next par of unassgned classes, each of whch s closest to one of the two clusterng groups and assgnng them to the correspondng group and the cluster center s updated. Thus, all the classes are assgned to one of the two possble groups of classes. The SVM bnary classfer s used to tran the samples n the root node of the decson tree. The classes from the frst clusterng group are assgned to the frst (left) sub tree, whle the classes from the second clusterng group are assgned to the second (rght) sub tree. The process of recursvely dvdng each of the groups nto two sub-groups contnues, untl there s only one class per group, whch defnes a leaf n the decson tree (Madzarov et al., 2009). Ths procedure leads to a bnary tree for the SVM-BDT that wll always be balanced, resultng n the best decson effcency. Fgure 5 shows the constructed SVM-BDT. After fndng the Eucldean dstance between all the semantc templates, class c 5 and c 7 are the farthest and assgned to the groups G 1 and G 2 respectvely. c 5 contans the mages of dnosaurs and c 7 contans the mages of roses. Closest to group G 1 s class c 3 and closest to group G 2 s c 6. In the next step, c 4 s assgned to group G 1 and C 8 to Fg. 5: SVM-BDT for Semantc Learnng group G 2. In the next step, c 10 s assgned to group G 1 and c 2 to group G 2. In the next step, c 1 s assgned to group G 1 and c 9 to group G 2. Ths completes the frst round of groupng that defnes the classes that wll be transferred to the left and rght sub tree of the root. The SVM bnary classfer n the root s traned by consderng the samples from the classes {c 5, c 3, c 4, c 10, c 1 as postve samples and samples from the classes {c 7, c 6, c 8, c 2, c 9 as negatve samples. The groupng procedure s repeated ndependently for the classes on the left and rght sub trees of the root, whch results n groupng {c 4, c 3, c 5 n G 11 and {c 1, c 10 n G 12 on the left sde of the sub tree and {c 8, c 2, c 9 n G 13 and {c 7, c 6 n G 14 on the rght sde of the sub tree. In the next level, {c 5, c 3 s grouped n G 21 on the left sde of the sub tree from the root and {c 2, c 9 n G 22 on the rght sde of the sub tree from the root. At each nonleaf node of the SVM-BDT, the SVM bnary classfer s used for tranng the postve and negatve samples. Class predcton usng the SVM-BDT and mage retreval: For the query mage, the domnant regon s automatcally found and the color and texture features are extracted for ths regon. Ths color and texture feature s gven as the nput to the SVM-BDT. The SVM bnary classfer at each non-leaf node s used to branch through the SVM-BDT and thereby the class label of the query mage s predcted. Thus, the statstcal smlarty measures can be appled between the query mage and only the mages of a partcular class. Ths reduces the search space as well as the searches for the mage, based on the hgh-level concept. 405

7 If the feature vector of the query mage s represented by equaton (8) and the feature vector of the target mage s gven by equaton (9), then the Eucldean dstance (Rahman et al., 2007; Rahman et al., 2005) between the query and target mage s gven by Eq. (10): q = (q 1, q 2,, q n ) (8) t = (t 1, t 2,, t n ) (9) D (q,t) (q t) (10) n 2 Euc = = 1 If the query mage q and the target mage t are assumed to be n dfferent classes and ther respectve denstes are p q (x) and p t (x) both defned on R 1, then, a popular measure of smlarty between the two Gaussan dstrbutons s the Bhattacharya dstance. The Bhattacharya dstance (Rahman et al., 2007; Fukunaga, 1990) for the query mage s calculated usng Eq. 11: 1 1 T q t Bhatt ( ) = μq μt D q,t ( ) ( + )/2 ( μq μ t) + In 2 q t q t (11) Where: µ q and µ t = The mean vectors Σ q and Σ t = The covarance matrces of the query mage q and target mage t respectvely. When all the classes have the same covarance matrces, the Bhattacharya dstance reduces to the Mahalanobs dstance (Rahman et al., 2007; Rahman et al., 2005) and t s calculated usng Eq. 12: T 1 D Maha (q,t) = ( μq μt ) ( μq μt ) (12) Where: µ = The mean vector = The covarance matrx The dstances are found between the feature vector of the query mage and the feature vector of the target mages. These dstances are sorted n the ncreasng order and the top k mages wth the least dstance are obtaned and the correspondng mages are dsplayed as the output. RESULTS In order to verfy the effectveness and effcency of the SRBIR system, experments were conducted on the COREL dataset consstng of 10,000 mages and ts subset wth 1000 mages of szes and The tranng sample for the SVM conssts of the fully labeled DB. The automatc segmentaton algorthm s tred for both the mage sets. For the 1000 mage data set, 86% of the mages are correctly segmented and the domnant obect n the mage s obtaned. The remanng 14% of the mages are not accurately segmented. In the case of 10,000 mages of the Corel mage data set, only 70% accuracy was acheved. For the constructon of the SVM-BDT, the semantc template of each category s calculated by obtanng the mean for each feature vector of the mages n each semantc category. Thus, ten semantc templates are calculated for the consdered 10 semantc categores. Then, the Eucldean dstances between every par of semantc templates are calculated. Ths dstance matrx s used for the classfcaton, whch contnues tll each group contans a sngle class. The SVM bnary classfer s used n tranng each level node, and t dvdes the group nto two sub groups. Only nne SVM bnary classfers are needed to perform the mult class classfcaton. The domnant regon of the query mage s found and the color-texture feature of the domnant regon s gven as the nput to the SVM-BDT classfcaton for predctng the semantc class of the query mage. The LIBSVM package has been used for mplementng the SVM-BDT n the SRBIR system (Hsu et al., 2003). The results obtaned usng the automatc segmentaton of the domnant foreground regon wth the SVM-BDT and the regon selecton wth the SVM-BDT are shown n Fgs. 6 and 7. DISCUSSION The results of the SRBIR system show that f we tran the SVM-BDT wth 100% of the tranng set mages, then the system produces 100% accuracy. If the SVM-BDT s traned wth 75% of the mages n the tranng data set, t produces 95.4% accuracy and f t s traned wth 50% of the mages n the tranng DB, the accuracy s 83%. The testng tme s the same for all the three types, whle the tranng tme ncreases as the tranng set sze ncreases. The tranng tme s a sngle tme operaton and hence, t could be neglected. The results are shown n Table

8 Fg. 6: Image Retreval based on the Segmentaton of the domnant foreground regon and the SVM-BDT Fg. 7: Image Retreval by regon selecton usng the SegTool and the SVM-BDT Table 1: Results for the COREL mage data set Tranng set Traned wth Traned wth Traned wth 50% of 75% of 100% of Measured n terms mages mages mages Accuracy rate (%) 83.00% 95.40% 10.00% Tranng tme (sec) Testng tme (sec) Fg. 8: Comparson wth dfferent nducton methods 407 Table 2: Comparson wth other decson tree learnng methods Classfcaton Method accuracy SRBIR wth domnant regon and SVM-BDT 100.0% RBIR wth regon selecton and SVM-BDT 86.0% RBIR wth DT-ST 74.6% ID3 63.5% C %

9 The results of the SRBIR system are compared wth those of the other decson tree learnng methods lke the DT-ST, ID3 and C4.5 and also wth the RBIR wth the regon selecton method and the SVM-BDT. The comparson s gven n Table 2 and the correspondng graph s shown n Fg. 8. It shows that the mplemented SRBIR produces hgher accuracy than the exstng RBIR technques. CONCLUSION The semantc regon based mage retreval looks for hgh-level features whch are close to the human nterpretaton of mages. The proposed system uses the automatc segmentaton of the domnant foreground regon from the mage whch provdes the hgh-level semantcs of the mage. The automatc segmentaton of the domnant regon reduces the nose n the segmentaton and the low-level features of the regon are mantaned wthout much dstorton. The low level features are extracted from the domnant regon of each of the mages and these features are used n tranng the SVM-bnary decson tree. The SVM-BDT s traned wth the color-texture template of each mage category. Ths SVM-BDT s used to predct the class label of the query mage. Thus, only the mages whose hgh level semantcs match wth those of the query mage are consdered for smlarty matchng. Ths reduces the testng tme and the accuracy of the system s promsng, when compared to the other regon-based mage retreval technques. If the query mage s not n the data set, the SRBIR system produces some msclassfcatons. Ths s the only lmtaton of the SRBIR system. Our future work ams at reducng such msclassfcatons and to provde the relevant mages from the database. Hence, the SRBIR framework usng the SVM-BDT can be used as a front-end for mage search, whch yelds hgh accuracy and takes less access tme. REFERENCES Suhasn, P.S., K.S.R. Krshna and I.V.M. Krshna, 2008, Graph based segmentaton n content based mage retreval. J. Comput. Sc., 4: DOI: /cssp Fukunaga, K., Introducton to Statstcal Pattern Recognton. 2nd Edn., Academc Press, London ISBN-10: pp: 592. Haralck, R.M., K. Shanmugam and I.Dnsten, Textural features for mage classfcaton. IEEE. Trans. Syst. Man. Cybern. 3: DOI: /TSMC Yogameena, B., E. Komagal, M. Archana and S.R. Abhakumar, Support vector machne-based human behavor classfcaton n crowd through proecton and star skeletonzaton. J. Comput. Sc., 6: DOI: /cssp Heath, M.D., S. Sarkar, T. Sanock and K.W. Bowyer, A robust vsual method for assessng the relatve performance of edge-detecton algorthms. IEEE. Trans. on Pattern Analyss Machne Intell., 19: DOI: / Hsu, C.W., C.C. Chang and C.J. Ln, A practcal gude to support vector classfcaton. Bonformatcs, 1: PubMed ID: Lu, S., H. Y, L.T. Cha and D. Raan, Adaptve herarchcal mult-class SVM classfer for texturebased mage classfcaton. IEEE Internatonal Conference on Multmeda and Expo, July 6-8, Technology Unversty, Sngapore, pp: 4-4. DOI: /ICME Lu, Y., D. Zhang, G. Lu and W.Y. Ma, A survey of content-based mage retreval wth hgh-level semantcs. Patt. Recog., 40: DOI: /.patcog Madzarov, G., D. Gorgevk, Mult-Class classfcaton usng support vector machnes n decson tree archtecture. Proceedng of the IEEE EUROCON 2009, May 18-23, St.-Petersburg, pp: DOI: /EURCON Madzarov, G., D. Gorgevk and I. Chorbev, A mult-class svm classfer utlzng bnary decson tree. Informatca, 33: Mao, Y., X. Zhou, D. P, Y. Sun and S.T.C. Wong, Multclass cancer classfcaton by usng fuzzy support vector machne and bnary decson tree wth gene selecton. J. Bomed. Botechnol., 2005: DOI: /JBB Abbad, N.K.A., N.S. Dahr, M.A. AL-Dhalm and H. Restom, Psorass detecton usng skn color and texture features. J. Comput. Sc., 6: DOI: /cssp Mezars, V., I. Kopatsars and M.G. Strntzs, An ontology approach to obect-based mage retreval. Proceedng of the Internatonal Conference on Image Processng, Sept , Unversty of Thessalonk, Greece, pp: DOI: /ICIP Rahman, M.M., P. Bhattacharya and B.C. Desa, Smlarty searchng n mage retreval wth statstcal dstance measures and supervsed learnng. Lect. Notes Comput. Sc., 3686: DOI: / _34 Rahman, M.M., P. Bhattacharya and B.C. Desa, A framework for medcal mage retreval usng machne learnng and statstcal smlarty matchng technques wth relevance feedback. IEEE. Trans. Inform. Technol. Bomed., 11: DOI: /TITB Zhang and G. Lu, Regon-based mage retreval wth hgh-level semantcs usng decson tree learnng. Patt. Recog., 41: DOI: /.patcog