Combination of Color and Local Patterns as a Feature Vector for CBIR

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1 Internatonal Journal of Computer Applcatons ( ) Volume 99 No.1, August 214 Combnaton of Color and Local Patterns as a Feature Vector for CBIR L.Koteswara Rao Asst.Professor, Dept of ECE Faculty of Scence and Technology IFHE Unversty Hyderabad, Inda D.Venkata Rao, Ph.D Prncpal Narasarao Pet Insttute of Technology Guntur Dstrct, Andhra Pradesh Inda ABSTRACT The local propertes of an mage can be acqured n many ways. Local Bnary Patterns(LBP) operator s one among them n whch a centre pxel s referenced wth the neghborng pxels to obtan a vector. However, the drectons are not consdered n ths method. The Drectonal Local Extrema Patterns() are used to encode the relatonshp between the reference pxel and ts neghbors by computng the edge nformaton n four drectons.in ths paper, we propose a new approach based on the combnaton of and color to derve the propertes those can be used n the process of retreval. 1. INTRODUCTION In recent years, due to the advances n the Internet and related felds, large number of mages are beng produced and stored across the globe. Hence there s a need for a system whch can search and ndex these mages n varous applcatons. The conventonal text based annotaton method of mage retreval becomes neffcent when the database sze s too large. Content based mage retreval(cbir), has drawn the attenton of researchers as an alternatve to the exstng methods n whch, the vsual contents such as color, texture, shape etc., are extracted for creaton of vector.smlarty between set of s of query mage and data base mage s measured based on whch more smlar mages are retreved. However, the effcency of any CBIR system depends on the extracton of s such as color, texture, shape etc., to ndex and retreve the mages [1][2]. It can be observed from the earler works n the feld of mage retreval that a sngle may not be suffcent to frame a vector.,but use of many s may lead to more complexty n the process[]. Among these s, color s one of the most dscrmnatng and powerful descrptor whch smplfes the object recognton [4] [5].Texture s another vsual s that refers to the nnate surface propertes of an object and ther relatonshp to the surroundng envronment. Many approaches to classfcaton and segmentaton of texture based on statstcal analyss, sgnal processng technques were proposed n the past.in [6],use of texture for classfcaton of mages was dscussed. Arvazhagan et al[7] proposed texture classfcaton usng wavelet transform. In [8] texture classfcaton and segmentaton was proposed usng wavelet packet frames and Gaussan mxture model. Gabor wavelets were used n texture classfcaton for rotaton nvarant s[9]. Dscrete Wavelet Transform (DWT) wth Kullback-Lebler dstance was proved better alternatve n gettng more texture nformaton [1]. However, a lmtaton of DWT based texture analyss s t can explore the s n,45 and 9 drectons only Contrbuton The drectonal local extrema pattern () extracts the drectonal edge nformaton based on local extrema n, 45, 9, and 15 drectons n an mage. The results can be further mproved by combnng ths wth color nformaton. In ths paper, we propose Combnaton of two s such as Texture and color to mprove the performance of the exstng Drectonal Local Extrema Pattern. The organzaton of ths paper s as follows. Secton 2 revews about local patterns and varatons. Secton explans the proposed work for retreval system. Expermental Results are shown n Secton 4. Secton 5 represents the concluson and future scope Related work A extracton approach based on Local Bnary Pattern was ntroduced by Ojala et. al [11].,Concept of LBP was extended to face recognton[12].however, LBP has the drawback of rotatonal nvarance n classfyng the structures. Local Dervatve pattern by consderng the n th order Local Bnary Pattern was proposed by Zhang et. al[1]. Subramanyam et al[14] proposed Drectonal Local Extrema Pattern as a vector for texture analyss of an mage. dffers from the exstng LBP and other extensons n terms of drectonal nformaton. 2. LOCAL PATTERNS AND VARIATIONS 2.1. Local Bnary Pattern (LBP) Local bnary pattern was ntroduced by T Ojala[11].In ths, the value of the centre pxel s consdered as threshold. The dfference between centre pxel value and ts neghbor s taken nto account to assgn a bnary or 1. The same procedure s repeated tll all the neghbors surroundng the centre pxel are covered n the computaton of the bnary pattern. LBP P, R p 1 p 1x k(gp gc)2,k(x) p x where g c represents the gray value of the center pxel and g p corresponds to gray value of P equally spaced pxels on crcumference of the crcle wth radus R Local Drectonal Pattern (LDP) Local Drectonal Pattern[15] s a method based on LBP whch uses the edge response values of neghborhood pxels to encode the texture n an mage. It assgns an eght bt bnary code to each pxel of an nput mage. A bnary value of 1 or s assgned dependng on the presence of an edge. 1

2 Internatonal Journal of Computer Applcatons ( ) Volume 99 No.1, August 214 LDP n 8 f(m m 1, x ) 2,f(x), x k Drectonal Local Extrema patterns () was ntroduced by Subrahmanyam et al [14].It descrbes the spatal structure of the local texture usng the local extrema of center gray pxel gc. The local extrema n four drectons are obtaned by calculatng the dfference between the centre pxel and all ts neghbors. In proposed, the local extrema n,45, 9 and 15 drectons are computed by takng the local dfference between the center pxel and ts neghbors as shown below. ' I (g ) I(g c ) I(g ); 1,2,...8 The local extremas are obtaned usng the equatons gven below. I (gc ) f (I (g ) I (g f (I (g ),I (g j j4 j4 )); j (1 / 45),45,9 1 I (g j) I (g )) else j4,15 ) The s defned (α=,45, 9 and15 ) as follows: (I(g )) c {I (gc);i(g);i(gz);...i (g8)) The detaled representaton of can be seen n fgure1.in the next step, the gven mage s converted to mages wth values rangng from to 511. After calculaton of, the whole mage s represented by buldng a hstogram based on the equaton mentoned below. H () N j1 N 1 2 k1 f 2 ((j,k), ); [,511] where the sze of nput mage s N 1 x N 2. The procedure for calculaton of for center pxel marked wth green color s presented n fg.1.the drectons are evaluated usng the local dfference between the center pxel and ts neghbors from whch the s are obtaned. As an example, the n 9 drecton for a center pxel marked n green color s shown n the fgure2. For a center pxel value 27, t can be observed that two neghborng pxels are leavng. Therefore, ths pattern s represented as 1. In the same way the rest of the bts of pattern are computed and the outcome s Smlarly, the s are computed for,45 and 15 drectons. (27) 1 (29) 2 (8) (87) 4 (88) 5 (1) 6 (78) 7 (85) 8 (6) P ( ) Q(45 ) R(9 ) S(15 ) Fg. 1 Illustraton of for x pattern Fg. 2 Example to compute n 9 drecton (111111) 2

3 Internatonal Journal of Computer Applcatons ( ) Volume 99 No.1, August 214 (a) (b) (c) (d) (e) Fgure: Example of maps; (a) Sample Image (b) (c) 45 (d) 9 (e) 15. PROPOSED C SYSTEM Query Image Database Image Convert RGB mage nto Grayscale Image separate the gven RGB mage nto three planes Convert RGB mage nto grayscale mage separate the gven RGB mage nto three planes Compute n,45, 9 and15 drectons Calculate the color moments to get color vector Compute n,45, 9 and15 drectons Calculate the color moments to get color vector Construct hstograms and concatenate to get a texture Construct hstograms and concatenate to get the vector Combne Texture and color Combne Texture and color Smlarty Computaton Image Retreval Fg.4 Proposed framework for content based Image retreval.1. Algorthm. Convert the RGB mage nto gray scale mage.. Compute the local extrema n,45, 9 and15 drectons.. v. Calculate the patterns n four drectons mentoned n step 2. Make a hstogram for the patterns obtaned n step and concatenate to get the texture vector v. To get the vector based on color, take the color moments n R,G,B planes separately and make a vector v. Combne these two s to get a vector that can be used n mage retreval..2. Query Matchng After the extracton of s, the vector for query mage s computed. In the same way, vector for the mages n the database s also computed. In order to select the more relevant mage to the query mage, the dstance between query and database mages s computed. 4. EXPERIMENTAL RESULTS Performance of the C s evaluated on standard corel-1k database [16]. The precson(p) and recall (R)values are computed as per the relatonshp mentoned here under.

4 Afrcans Beach Buldng Buses Dnosaur Elephant Flower Horse Mountan Food Average Recall Average Precson Internatonal Journal of Computer Applcatons ( ) Volume 99 No.1, August 214 The results for ten categores of the data base are specfed n the table 1. Table 1: Comparson of precson values for and C Category Exstn g + Color Afrcans Beach Buldng Buses Dnosaur Elephant Flower Horse Mountan Food Average Precson (%) Average Precson + Color Table 2: Comparson of recall values for and C Category + Color Afrcans Beach 7. 9 Buldng Buses Dnosaur Elephant Flower Horse Mountan 29. Food Average Recall (%) The comparsons n terms of average precson and recall are gven n the graph gven below. Fgure 5. category- wse performance n terms of precson Average Recall Fgure 6. category- wse performance n terms of recall 5. CONCLUSION AND FUTURE SCOPE It s proved from the results that the precson and Recall values of the proposed method are better than the exstng drectonal patterns. Ths work can be further extended by combnng the C wth transforms such as Contourlets or Curvelet transforms to get the texture nformaton n more drectons. 6. REFERENCES [1] R. Datta, D Josh J.L and J. Wang, Image Retreval- Ideas, nfluences and trends of the new age, ACM Computng surveys,vol.4,no.2,pp [2] AM smeulders,m Worrng,S Santn,A Gupta&R Jan, content based Image retreval at the end of early years IEEE Transactons aon PAMI 22(12),pp ,2 + Color 4

5 Internatonal Journal of Computer Applcatons ( ) Volume 99 No.1, August 214 [] Y Ru,T S Huang&S F Chang, Image retreval: current technques, promsng drectons& open ssues. Journal of vsual communcatons& Image representaton 1(4):pp 9-62,1999. [4] Dgtal Image Processng Gonzalez& Woods thrd edton [5] M J Swan,D H Ballard, Color Indexng, Internatonal Journal of computer vson (1991) 11-2 [6] R M Haralck,K Shanmugam& dnsten, texture s for mage classfcaton,ieee transactons on system, man and cybernetcs vol,smc-8,pp , 197 [7] S Arvazhagan and L Ganesan, Texture classfcaton usng wavelet transform( ) vol.24,ssue 9-1,June 2 [8] Soo chang Km,Tae Jn Kang, Texture classfcaton and segmentaton usng wavelet packet frame and Gaussan mxture model vol 4,ssue 4,aprl 27, elsever [9] S Arvazhagan and L Ganesan, Texture classfcaton usng Gabor wavelets based rotaton nvarant s,vol 27,ISSUE 16, December 26 ( ) [1] Do MN,Vettrl M(22) Wavelet-based texture retreval usng generalzed Gaussan densty and Kullback-lebler dstance, IEEE Transactons on Image processng.vol 11(2). [11] Ojala T,Petkanen M,Harwood D (1996) A comparatve study of texture measures wth classfcaton based on dstrbutons. J Pattern Recognton 29(1):51-59 [12] A Hadd,T Ahonen and M Petkanen, Face analyss usng local bnary patterns, n handbook of Texture analyss, Imperal college press London 28,pp 47-7 [1] Zhang B, Gao Y,Zhao S, Lu J (21) Local dervatve pattern versus local bnary pattern: Face Recognton wth hgher-order local pattern descrptor, IEEE Trans Image Process 19(2):5-544 [14] Subrahmanyam Murala, R.P. Maheswar, R.Balasubramanan Drectonal local extrema pattern: a new descrptor for content based mage retreval(212) [15] Jabd,,Kabr, Chae Kyung H, Local Drectonal Pattern for face recognton IEEE conference 21 IJCA TM : 5

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