Local Binary Pattern-based Noise Robust Feature for Texture Classification
Abstract: The presence of noise degrades the local binary pattern-based classification efficiency. In the present work, a modified local binary pattern ( —modified noise robust local binary pattern) based classification is proposed. In this, a local binary pattern-based feature is modified, which also captures macrostructure information, whereas the existing features capture microstructure texture information only. The new feature is tested on Outex_TC_00010, Outex_TC_00012 and Brodatz datasets for rotation invariant and noise robust texture classification. The texture images are degraded with multiplicative noise, to evaluate noise robustness of the feature. Nearest neighbour classifier is used for classification which minimises chi-square distance. The proposed feature gives promising results as it is rotation invariant, robust to noise and gives high classification accuracy, especially at high levels of noise.
Keywords: Texture classification, local binary pattern, feature extraction, histogram
Cite this Article: Simarjot Kaur Randhawa, Ramesh Kumar Sunkaria. Local Binary Pattern-based Noise Robust Feature for Texture Classification. Journal of Image Processing & Pattern Recognition Progress. 2019; 6(3): 31–47p.
Laine A, Fan J. Texture classification by wavelet packet signatures. IEEE Transactions on pattern analysis and machine intelligence. 1993;15(11): 1186-1191p.
Do MN, Vetterli M. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE transactions on image processing. 2002;11: 146-158p.
Pi MH, Tong CS, Choy SK, et al. A fast and effective model for wavelet subband histograms and its application in texture image retrieval. IEEE transactions on image processing. 2006;15(10): 3078-3088p.
Ji H, Yang X, Ling H, et al. Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Transactions on Image Processing. 2013; 22(1): 286-299p.
Manjunath B S, Ma WY. Texture features for browsing and retrieval of image data. IEEE Transactions on pattern analysis and machine intelligence. 1996; 18(8), 837-842p.
Jain AK, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. Pattern recognition. 1991;24(12): 1167-1186p.
Arivazhagan S, Ganesan L, Kumar TGS. Texture classification using ridgelet transform. Pattern Recognition Letters. 2006;27(16): 1875-1883p.
Davis LS. Polarograms: a new tool for image texture analysis. Pattern Recognition. 1981; 13(3): 219-223p.
Qi X, Xiao R, Li CG, Oiao Y, et al. Pairwise rotation invariant co-occurrence local binary pattern. IEEE transactions on pattern analysis and machine intelligence. 2014;36(11): 2199-2213p.
Liu L, Lao S, Fieguth PW, et al. Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing. 2016;25(3): 1368-1381p.
Silvén O, Niskanen M, Kauppinen H. Wood inspection with non-supervised clustering. Machine Vision and Applications. 2003;13(5-6): 275-285p.
Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with local binary patterns. Pattern recognition. 2009;42(3): 425-436p.
Murala S, Maheshwari RP, and Balasubramanian R. Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing. 2012;21(5): 2874-2886p.
Satpathy A, Jiang X, Eng HL. LBP-based edge-texture features for object recognition. IEEE Transactions on Image Processing. 2014;23(5): 1953-1964p.
Asery R, Sunkaria RK. A novel local octa-pattern feature descriptor for image retrieval. Signal, Image and Video Processing. 2018;12(1): 151-159p.
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence. 2002;24(7): 971-987p.
Jin H, Liu Q, Lu H, et al. Face detection using improved LBP under Bayesian framework. Third International Conference on Image and Graphics (ICIG'04). 2004 Dec; 306-309p.
Hafiane A, Seetharaman G, Zavidovique B. Median binary pattern for textures classification. In International Conference Image Analysis and Recognition. 2007 August; 387-398p.
Liao S, Law MWK, Chung ACS. Dominant local binary patterns for texture classification. IEEE transactions on image processing. 2009;18(5): 1107-1118p.
Guo Z, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing. 2010;19(6):1657-1663, 2010.
Baochang Z, Gao Y, Zhao S, et al. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE transactions on image processing. 2010;19(2), 533-544p.
Guo Z, Zhang L, Zhang D. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern recognition. 2010;43(3), 706-719p.
Tan X, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing. 2010;19(6): 1635-1650p.
Qian X, Hua XS, Chen P, et al. PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition. 2011;44(10-11): 2502-2515p.
Liu L, Zhao L, Long Y, et al. Extended local binary patterns for texture classification. Image and Vision Computing. 2012;30(2): 86-99p.
Guo Y, Zhao G, PietikäInen M. Discriminative features for texture description. Pattern Recognition. 2012;45(10): 3834-3843p.
Zhao Y, Huang DS, Jia W. Completed local binary count for rotation invariant texture classification. IEEE transactions on image processing. 2012;21(10): 4492-4497p.
Zhang J, Lian, J, Zhao H. Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE transactions on image processing. 2013; 221(1): 31-42p.
Yuan F. Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification. Digital Signal Processing. 2014;26: 142-152p.
Hong X, Zhao G, Pietikäinen M, et al. Combining LBP difference and feature correlation for texture description. IEEE Transactions on Image Processing. 2014;23(6), 2557-2568p.
Liu L, Long Y, Fieguth PW, et al. BRINT: binary rotation invariant and noise tolerant texture classification. IEEE transactions on Image Processing. 2014;23(7): 3071-3084p.
Ryu J, Hong S, Yang HS. Sorted consecutive local binary pattern for texture classification. IEEE Transactions on Image Processing. 2015;24(7): 2254-2265p.
Ramalho GLB, Ferreira DS, Rebouças Filho PP, et al. Rotation-invariant feature extraction using a structural co-occurrence matrix. Measurement. 2016;94: 406-415p.
Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Pattern Recognition, Conference A: Computer Vision & Image Processing, Proceedings of the 12th IAPR International Conference. 1994 Oct;1: 582-585p.
Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996;29(1): 51-59p.
Leutenegger S, Chli M, Siegwart R. BRISK: Binary robust invariant scalable keypoints. Computer Vision (ICCV) IEEE International Conference. 2011 Nov; 2548-2555p.
Alahi A, Ortiz R, Vandergheynst P. Freak: Fast retina keypoint. Computer vision and pattern recognition (CVPR). 2012: 510-517p, IEEE conference, 2012.
Calonder M, Lepetit V, Ozuysal M, et al. BRIEF: Computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34: 1281-1298p.
Ojala T, Maenpaa T, Pietikainen M, et al. Outex-new framework for empirical evaluation of texture analysis algorithms. Pattern Recognition, 16th International Conference. 2002;1: 701-706p.
Brodatz, P. Textures: a photographic album for artists and designers. Dover Pubns; 1966.
Varma M, Zisserman A. A statistical approach to texture classification from single images. International journal of computer vision. 2005;62(1-2): 61-81p.
Varma M, Zisserman A. A statistical approach to material classification using image patch exemplars. IEEE transactions on pattern analysis and machine intelligence. 2009;31(11): 2032-2047p.
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