Color Texture Classfaton usng Modfed Loal Bnary Patterns based on Intensty and Color Informaton Shvashankar S. Department of Computer Sene Karnatak Unversty, Dharwad-580003 Karnataka,Inda shvashankars@kud.a.n Madhur R. Kagale Department of Computer Sene Karnatak Unversty, Dharwad-580003 Karnataka, Inda madhur.kagale@gmal.om Prakash S. Hremath Department of Computer Sene KLE Tehnologal Unversty, BVBCET, Hubbaall-580031 Karnataka, Inda hremathps53@yahoo.om Abstrat Ths paper presents a olor texture desrptor, Modfed Loal Bnary Pattern (MLBP) based on ombned ntensty and olor nformaton. It establshes the exstene of orrelaton between the ntensty and olor hannel nformaton along wth ther orrespondng neghbors. Bn values of MLBP mage hstogram are used as olor texture desrptors. The lassfaton performane of the proposed olor texture desrptor s evaluated on VsTex and Outex datasets, for dfferent olor spaes, namely RGB, HSV, YCbCr, La*b* usng k-nearest neghborhood lassfer. The lassfaton results obtaned by the proposed texture desrptor are ompared wth the results obtaned by the onventonal Loal Bnary Patterns (LBP) method. The expermental results ndate that the proposed olor texture desrptors enhane the lassfaton performane. Keywords- Color texture; Texture lassfaton; Intensty and olor hannel; Hstogram features I. INTRODUCTION In many mahne vson and mage proessng applatons, texture analyss s an mportant and useful faet of study. Texture analyss methods have been exploted n varous applatons that nvolve automated nspeton, doument mage proessng and remote sensng [1]. Over the past deade, the study of jont olor texture has been a popular approah to olor texture analyss. The extra nformaton present n olor mages ompared to ther gray mage equvalent allows onstrutng an mage analyss system wth hgher performane [2, 3]. That s why a wde varety of gray sale texture desrptors have been extended to lassfy olor textures [4-8]. The Loal Bnary Pattern (LBP) operator, frst proposed by Ojala [9], desrbes the loal propertes of textures present n gray level mages. In reent years, Loal Bnary Patterns (LBP) s the most suessful approah employed n many areas of pattern reognton and omputer vson [10-12]. Maenppa and Petkanen [13] have extended gray sale approah to olor mages, where olor hannels are ombned at feature level by onatenatng LBP hstogram from olor hannels and proessed separately usng LBP. An approah based on separate proessng of omplementary olor and pattern nformaton s proposed by Petkanen, Mäenpää and Vertola [14], wheren they have used olor hstograms to dsrmnate olor nformaton and LBP to provde texture nformaton. Porebsk, Vandenbrouke and Maare [15] have proposed an approah for olor texture lassfaton by usng Haralk features extrated from o-ourrene matres omputed from LBP mages. Zhu, Bhot and Chen [16] mplemented olor orthogonal ombnaton of LBP, desrptor by onatenatng olor orthogonal ombnaton of LBP hstograms and olor hannels. The extenson of the onventonal LBP method to a three Dmensonal Loal Bnary Patterns desrptor produes three new olor mages for enodng both olor and texture nformaton of an mage usng the dfferent olor hannels [17]. The am of the present researh work s to analyze the smlarty of the olors and ther dstrbutons at the same tme by proposng a olor texture desrptor. A olor texture desrptor based on ombned ntensty and olor nformaton alled Modfed Loal Bnary Pattern (MLBP) s proposed. MLBP mage hstogram bn values are used as the olor texture desrptors. The lassfaton performane of the proposed olor texture desrptor s evaluated on a set of VsTex [18] and Outex [19] olor mages, for dfferent olor spaes namely RGB, HSV, YC b C r and La*b*. The expermental results obtaned by usng the proposed desrptor are ompared wth the results obtaned by usng the onventonal LBP method [8]. Vol. 9 No.12 De 2017 695
Ths paper s organzed as follows: The seton II brefly ntrodues Loal Bnary Patterns. The proposed olor texture feature omputaton method Modfed Loal Bnary Pattern (MLBP) s dsussed n the Seton III. In the Seton IV, the texture tranng and lassfaton are explaned. Experments and lassfaton results are presented n the Seton V. Fnally, the Seton VI onludes the dsusson. II. LOCAL BINARY PATTERNS Let texture T n a loal N-neghborhood of a pxel f s defned as the jont dstrbuton funton of all pxels n loal texture regon of a gray mage. T s represented as n (1), T t( f ; f 0, f1,..., f,..., f N 1 ) where f s the gray value of the enter pxel of loal texture regon, f s the gray value of th neghborng pxel of loal texture regon. For =8, the Fg.1 depts the 8-neghborhood of pxel f. f 3 f 2 f 1 f 4 f f 0 f 5 f 6 f 7 Fgure 1. 8-neghborhood of pxel f. Next the fatorzed jont dstrbuton funton obtaned by subtratng the enter pxel value f from eah pxel value n rular neghborhood an be represented by (2), T t f ) t( f f, f f,..., f f ) (2) ( 0 1 N 1 In (2), t f ) dstrbuton s not useful for texture analyss. The jont dfferene dstrbuton, whh s ndependent of t f ), an be used as texture representaton n (3), T t f f, f f,..., f f ) (3) ( 0 1 N 1 The T an be represented by usng the (4), T t g( f f ), g( f f ),...,..., g( f f )) 4 ( 0 1 N 1 0, p 0 where g( p) 1, p 0 Equaton (4) generates the loal bnary pattern (LBP) around the enter pxel f. Further, the sum of produt of bnary values and orrespondng weghted values of all surroundng pxels, as gven n (5), s set as the new enter pxel value P n T. N 1 0 P g( f f ) * 2 (5) III. FEATURES FROM MODIFIED LOCAL BINARY PATTERN (MLBP) IMAGE BASED ON INTENSITY AND COLOR INFORMATION The olor texture mage ontans R, G and B hannels. These hannels are used to transform olor mage to ntensty mage usng the (6) [8], I=0.299 R+0.587 G+0.114 B (6) Let us onsder a par of mages (I, R), for a entral pxel I n I and ts orrespondng entral pxel R n R. Consder ther 8-neghborhood I N, R N respetvely as shown n the Fg. 2, Vol. 9 No.12 De 2017 696
I 4 I 3 I 2 R 4 R 3 R 2 I 5 I I 1 R 5 R R 1 I R Fgure 2. 8- neghborhood of pxel I n I and ts orrespondng pxel R n R. The modfed loal bnary pattern g s onstruted usng I and R as shown n the (7), 1; f max(mn( I, R ),mn( R, I )) mn( I, R ) g 0; otherwse where = 1,2,,N and N=8 s number of neghborng pxels. The new entral pxel value P s omputed usng the (8), N 1 P g * 2 (8 1 I 6 I 7 I 8 R 6 R 7 R 8 The Fg. 3 llustrates the above proedure to alulate the entral pxel value P whh obvously les n the range 0 to 255. 137 147 120 139 146 140 134 148 140 132 135 163 158 168 153 145 155 98 I R (7) 0 0 1 1 0 0 0 1 Bnary Pattern * 8 4 2 16 1 32 64 128 Weghts P = (2+16+128) = 146 Fgure 3. Calulaton of entral pxel value P. Fgure 4. Shemat dagram of the proposed olor texture feature extraton usng MLBP method. Vol. 9 No.12 De 2017 697
The above proedure s repeated for all the pxels of the entre mage, whh results n Modfed LBP mage (MLBP) MLBP I,R based on the ombnaton of Intensty (I) and Red olor (R) nformaton. Smlarly, we onstrut MLBP mages usng the other two ombnatons, namely MLBP I,G, MLBP I,B. The features are extrated from MLBP mage as desrbed below: 1) Construt Hstogram (H) for the MLBP mage. 2) Obtan the Cumulatve Hstogram (CH) for H. 3) Construt Normalzed Cumulatve Hstogram NCH = CH/Sum (CH). The bn values of NCH form the texture feature vetor v=(b 0,b 1,b 2,,b 255 ). The Fg. 4 depts the proposed olor texture feature extraton usng MLBP method. The features are extrated from the ombnatons of ntensty and olor nformaton of HSV, YC b C r and La*b* olor spaes. The results are ompared wth RGB olor spae. IV. TEXTURE TRAINING AND CLASSIFICATION In texture tranng phase, texture features are extrated from tranng sample usng the proposed feature extraton method MLBP. These texture features are stored n the feature lbrary, whh are further used for texture lassfaton. In texture lassfaton phase, the texture feature vetor s extrated from the test sample usng the proposed feature extraton method MLBP, and then ompared wth the orrespondng feature vetor of all the texture samples stored n the feature lbrary usng l 1 -norm as the dstane metr [20] gven by (9), D ( p, q) p q 9 where p, q are the th omponents of feature vetors p and q respetvely. The test sample s lassfed usng the k-nearest neghbor (k-nn) lassfer. In the present study, t s hosen as k = 1 [21]. V. EXPERIMENTS AND DISCUSSION Experments are performed on three dfferent olor texture datasets. The sets nlude 164 olor textures of sze 128*128 avalable from the Vson Texture (VsTex) dataset [18], tranng and testng samples are hosen as n [22], and two sets of 68 olor textures (Outex_TC_00013 and Outex_TC_00014) from the Outex texture dataset[19]. The dfferent olor spaes, namely RGB, HSV, YC b C r, and La*b* are onsdered for the evaluatons of the proposed method. The results based on dfferent sample szes are presented for omparatve analyss of texture lassfaton. TABLE I. CLASSIFICATION ACCURACY (%) OF COLOR TEXTURE USING PROPOSED MLBP METHOD. Color Spaes RGB HSV YC b C r La*b* MLBP based features wth Datasets ntensty and olor hannel ombnatons VsTex Outex _TC_00013 Outex _TC_00014 MLBP I,R 95.54 67.50 24.04 MLBP I,G 94.22 69.71 18.53 MLBP I,B 95.54 58.08 12.21 MLBP I,R, MLBP I,G 96.43 78.82 17.35 MLBP I,R, MLBP I,B 97.05 76.03 18.75 MLBP I,G, MLBP I,B 96.68 74.12 14.26 MLBP I,R, MLBP I,G, MLBP I,B 97.23 82.33 16.76 MLBP I,H 79.28 63.97 13.82 MLBP I,S 94.18 55.44 12.50 MLBP I,H, MLBP I,S 95.59 76.18 15.51 MLBP I,Cb 79.23 31.03 19.34 MLBP I,Cr 78.41 45.29 18.24 MLBP I,Cb, MLBP I,Cr 84.59 40.00 18.01 MLBP I,a* 76.86 45.44 21.62 MLBP I,b* 75.77 31.47 19.93 MLBP I,a*, MLBP I,b* 80.31 39.26 19.04 Vol. 9 No.12 De 2017 698
TABLE II. COMPARISON OF CLASSIFICATION ACCURACY (%) OBTAINED BY USING PROPOSED MLBP METHOD AND CONVENTIONAL LBP METHOD FOR VISTEX DATASET. Sze of sample Proposed MLBP method LBP method[8] 120 98.96 97.62 110 98.44 96.42 100 97.23 93.52 80 95.82 91.20 50 89.24 79.49 20 38.96 28.01 10 11.06 7.70 The lassfaton results of experments are presented n the Tables I and II. The entres n the table shows the lassfaton auray (%) averaged over 10 experments for dfferent ombnatons of ntensty and olor hannels, for an angle=0 and dstane=1. In the Table I, frst olumn ndates the dfferent olor spaes used n the experments; seond olumn shows the ombnatons of ntensty and olor hannels. The thrd, fourth and ffth olumns show the lassfaton auray (%) on VsTex, Outex _TC_00013 and Outex _TC_00014 datasets respetvely. The results show that maxmum lassfaton auray of 97.23% and 82.33% s obtaned for VsTex and Outex_TC_00013 datasets n RGB olor spae. The maxmum of 24.04% lassfaton auray s obtaned when the ntensty and red olor hannel of RGB olor spae s used for Outex_TC_00014. It s learly seen n Table I, that the proposed method gves the better results n RGB olor spae as ompared to other olor spaes. The Table II shows the omparatve results obtaned usng VsTex dataset for the proposed method (MLBP I,R, MLBP I,G, MLBP I,B ) and the onventonal LBP method (LBP R, LBP G, LBP B ) [8], here all the olor hannels of RGB olor spae are used. It s observed from the results that the ombnatons of ntensty wth olor hannels mprove the lassfaton auray. It reveals that the proposed method outperform LBP method for dfferent sample szes. Even f the 50% of the pattern mage s onsdered, the better lassfaton an be expeted usng the proposed method as ompared to the onventonal LBP method. Thus t establshes the exstene of orrelaton between ntensty and olor hannel nformaton along wth ther orrespondng neghbors. VI. CONCLUSION A olor texture desrptor based on ombned ntensty and olor nformaton alled Modfed Loal Bnary Pattern (MLBP) s proposed. MLBP mage hstogram bn values are used as the olor texture desrptors. The experments are performed on benhmark datasets, namely, VsTex and Outex datasets, for dfferent olor spaes, namely, RGB, HSV, YC b C r and La*b*. The proposed olor texture desrptors gve the better lassfaton results for RGB olor spae along wth ntensty values and t also outperform the onventonal Loal Bnary Patterns (LBP) method. The results suggest that the proposed olor texture desrptors have the potental for use n real-world applatons nvolvng reognton of patterns n dgtal mages. REFERENCES [1] M. Tueryan, and A. K. Jan, Texture analyss, Handbook of pattern reognton and omputer vson, vol.2, pp.235-276, 1993. [2] Drmbarean, and P. F. Whelan, Experments n olour texture analyss, Pattern reognton letters, vol. 22(10), pp. 1161-1167, 2001. [3] Palm, Color texture lassfaton by ntegratve Co-ourrene matres, Pattern Reognton, vol. 37(5), pp. 965-976, 2004. [4] L. Wang, and J. Lu, Texture lassfaton usng multresoluton Markov random feld models, Pattern Reognton Letters, vol. 20(2), pp.171-182, 1999. [5] S. Grgoresu, N. Petkov, and P. Kruznga, Comparson of texture features based on Gabor flters, IEEE Trans. Image Proess, vol. 11(10), pp. 1160-1167, 2002. [6] S. Arvazhagan, and L.Ganesan, Texture lassfaton usng wavelet transform, Pattern Reognton Letters, vol. 24(9), pp. 1513-1521, 2003. [7] R. Haral, and K. Shanmugam, Textural features for mage lassfaton, IEEE Transatons on systems, man, and ybernets, vol. 3(6), pp. 610-621, 1973. [8] T. Ojala, M. Petkanen, and T. Maenpaa, Multresoluton gray-sale and rotaton nvarant texture lassfaton wth loal bnary patterns, IEEE Transatons on Pattern Analyss and Mahne Intellgene, vol. 24(7), pp. 971-987, 2002. do: 10.1109/TPAMI.2002.1017623. [9] T. Ojala, M. Petkanen, and D. Harwood, A omparatve study of texture measures wth lassfaton based on featured dstrbutons, Pattern Reognton, vol. 29(1), pp. 51-59, 1996. [10] M. Petkänen, A. Hadd, G. Zhao, and T. Ahonen, Lbp n dfferent applatons, In Computer Vson Usng Loal Bnary Patterns, Sprnger London, pp. 193-20, 2011. [11] J. Lu, V. E. Long, X. Zhou, and J. Zhou, Learnng ompat bnary fae desrptor for fae reognton, IEEE transatons on pattern analyss and mahne ntellgene, vol. 37(10), pp. 2041-2056, 2015. [12] M. Häfner, M. Ledlgruber, A. Uhl, A. Vése, and F. Wrba, Color treatment n endosop mage lassfaton usng mult-sale loal olor vetor patterns, Medal mage analyss, vol. 16(1), pp. 75-86, 2012. Vol. 9 No.12 De 2017 699
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