Research and Perspective on Local Binary Pattern
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摘要: 针对当前局部二值模式(Local binary pattern, LBP)方法表现出的理论和实际应用价值, 系统综述了在纹理分析和分类、人脸分析和识别以及其他检测与应用中的各种LBP 方法.首先, 简要概述了LBP方法的原理, 主要分析了LBP 方法中的阈值操作并介绍了统一模式和旋转不变性模式.其次, 分别对纹理分析和分类中的LBP方法、人脸分析和识别中的LBP方法以及其他检测与应用中的LBP方法等三个方面进行了详细的梳理和评述.最后, 分析了LBP方法在应用中依旧存在的重要问题并指出了未来的研究方向.Abstract: In view of the theoretical and practical value of local binary pattern (LBP), the various LBP methods in texture analysis and classification, face analysis and recognition, and other detection applications are reviewed. Firstly, the principle of LBP method is briefly discussed, which mainly analyses the threshold operation, the uniform pattern and rotation invariant pattern in LBP method. Secondly, the texture analysis and classification of the LBP method, face analysis and recognition of the LBP method and other detection applications of the LBP method are particular combed and commented. Finally, the existing important problems of the LBP method are analyzed and the future for the LBP method is pointed out.
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Key words:
- Local binary pattern (LBP) /
- feature extraction /
- texture analysis /
- face analysis /
- object detection
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[1] Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 1996, 29(1): 51-59 [2] Pietikäinen M, Ojala T, Xu Z. Rotation-invariant texture classification using feature distributions. Pattern Recognition, 2000, 33(1): 43-52 [3] Ojala T, Valkealahti K, Oja E, Pietikäinen M. Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition, 2001, 34(3): 727-739 [4] Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987 [5] Pietikäinen M, Hadid A, Zhao G Y, Ahonen T. Computer Vision Using Local Binary Patterns. Berlin: Springer-Verlag, 2011. 193-202 [6] Hafiane A, Seetharaman G, Zavidovique B. Median binary pattern for textures classification. In: Proceedings of the 2007 International Conference on Image Analysis and Recognition. Montreal, Canada: Springer, 2007. 387-398 [7] Guo Z H, Zhang L, Zhang D, Zhang S. Rotation invariant texture classification using adaptive LBP with directional statistical features. In: Proceedings of the 17th IEEE International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 285-288 [8] 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): 2032-2047 [9] Guo Z H, 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 [10] Guo Z H, Zhang L, Zhang D. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition, 2010, 43(3): 706-719 [11] He C, Ahonen T, Pietikäinen M. A Bayesian local binary pattern texture descriptor. In: Proceedings of the 19th International Conference on Pattern Recognition. Tampa, USA: IEEE, 2008. 1-4 [12] Brodatz P. Textures: A Photographic Album for Artists and Designers. New York: Dover Publications, 1966. 1-112 [13] Liao S, Law M W K, Chung A C S. Dominant local binary patterns for texture classification. IEEE Transactions on Image Processing, 2009, 18(5): 1107-1118 [14] Ojala T, Mäenpää T, Pietikäinen M, Viertola J, Kyllönen J, Huovinen S. Outex-new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of the 16th International Conference on Pattern Recognition. Quebec, Canada: IEEE, 2002. 701-706 [15] Nanni L, Lumini A, Brahnam S. Survey on LBP based texture descriptors for image classification. Expert Systems with Applications, 2012, 39(3): 3634-3641 [16] Mäenpää T, Ojala T, Pietikäinen M, Soriano M. Robust texture classification by subsets of local binary patterns. In: Proceedings of the 2000 International Conference on Pattern Recognition. Barcelona, Spain: IEEE, 2000. 947-950 [17] Mäenpää T, Pietikäinen M. Multi-scale binary patterns for texture analysis. In: Proceedings of the 13th Scandinavian Conference on Image Analysis. Lecture Notes in Computer Science. Berlin, Germany: Springer, 2003. 885-892 [18] Raja Y, Gong S G. Sparse multiscale local binary patterns. In: Proceedings of the 2006 British Machine Vision Conference. Edinburgh, UK: BMVA, 2006. 1-10 [19] He Y G, Sang N, Gao C X. Pyramid-based multi-structure local binary pattern for texture classification. In: Proceedings of the 10th Asian Conference on Computer Vision. Berlin, Heidelberg: Springer, 2011. 133-144 [20] Ahonen T, Pietikäinen M. Soft histograms for local binary patterns. In: Proceedings of the 2007 Finnish Signal Processing Symposium. Oulu, Finland: FINSIG, 2007. 1-4 [21] Iakovidis D K, Keramidas E, Maroulis D. Fuzzy local binary patterns for ultrasound texture characterization. In: Proceedings of the 5th International Conference on Image Analysis and Recognition. Berlin, Heidelberg: Springer, 2008. 750-759 [22] Zhang L, Zhang L, Guo Z H, Zhang D. Monogenic-LBP: a new approach for rotation invariant texture classification. In: Proceedings of the 17th International Conference on Image Processing. Hong Kong, China: IEEE, 2010. 2677-2680 [23] Zhou H, Wang R S, Wang C. A novel extended local-binary-pattern operator for texture analysis. Information Sciences, 2008, 178(22): 4314-4325 [24] Liu L, Zhao L J, Long Y L, Kuang G Y, Fieguth P. Extended local binary patterns for texture classification. Image and Vision Computing, 2012, 30(2): 86-99 [25] Khellah F M. Texture classification using dominant neighborhood structure. IEEE Transactions on Image Processing, 2011, 20(11): 3270-3279 [26] Guo Y M, Zhao G Y, Pietikäinen M. Discriminative features for texture description. Pattern Recognition, 2012, 45(10): 3834-3843 [27] Guo Y, Zhao G, Pietikäinen M, Xu Z. Descriptor learning based on Fisher separation criterion for texture classification. In: Proceedings of the 2011 Asian Conference on Computer Vision. Queenstown, New Zealand: Springer, 2011. 185-198 [28] Zhao Y, Huang D S, Jia W. Completed local binary count for rotation invariant texture classification. IEEE Transactions on Image Processing, 2012, 21(10): 4492-4497 [29] Fathi A, Naghsh-Nilchi A R. Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 2012, 33(9): 1093-1100 [30] Mäenpää T, Pietikäinen M. Classification with color and texture: jointly or separately? Pattern Recognition, 2004, 37(8): 1629-1640 [31] Pietikäinen M, Mäenpää T, Viertola J. Color texture classification with color histograms and local binary patterns. In: Proceedings of the 2008 International Workshop on Texture Analysis and Synthesis. Copenhagen, Denmark: IWTAS, 2002. 109-112 [32] Porebski A, Vandenbroucke N, Macaire L. Haralick feature extraction from LBP images for color texture classification. In: Proceedings of the 2008 International Workshops on Image Processing Theory, Tools and Applications. Sousse, Tunisia: IEEE, 2008. 1-8 [33] Zhang J, Liang J M, Zhao H. Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Transactions on Image Processing, 2013, 22(1): 31-42 [34] Zhao G Y, Ahonen T, Matas J, Pietikäinen M. Rotation-invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing, 2012, 21(4): 1465-1477 [35] Ojansivu V, Heikkilä J. Blur insensitive texture classification using local phase quantization. In: Proceedings of the 2008 International Conference on Image and Signal Processing. Cherbourg-Octeville, France: Springer, 2008. 236-243 [36] Lategahn H, Gross S, Stehle T, Aach T. Texture classification by modeling joint distributions of local patterns with Gaussian mixtures. IEEE Transactions on Image Processing, 2010, 19(6): 1548-1557 [37] Chen J, Shan S G, He C, Zhao G Y, Pietikäinen M, Chen X L, Gao W. WLD: a robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1705-1720 [38] Liu L, Fieguth P, Clausi D, Kuang G Y. Sorted random projections for robust rotation-invariant texture classification. Pattern Recognition, 2012, 45(6): 2405-2418 [39] Liao S, Chung A C S. Face recognition by using elongated local binary patterns with average maximum distance gradient magnitude. In: Proceedings of the 8th Asian Conference on Computer Vision. Tokyo, Japan: Springer, 2007. 672-679 [40] Jin H L, Liu Q S, Lu H Q, Tong X F. Face detection using improved LBP under Bayesian framework. In: Proceedings of the 3rd International Conference on Image and Graphics. Hong Kong, China: IEEE, 2004. 306-309 [41] Petpon A, Srisuk S. Face recognition with local line binary pattern. In: Proceedings of the 5th International Conference on Image and Graphics. Xi'an, China: IEEE, 2010. 533-539 [42] Tan X Y, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Proceedings of the 3rd International Conference on Analysis and Modeling of Faces and Gestures. Rio de Janeiro, Brazil: Springer, 2007. 168-182 [43] Liao S C, Zhu X X, Lei Z, Zhang L, Li S Z. Learning multi-scale block local binary patterns for face recognition. In: Proceedings of the 2007 International Conference on Biometrics. Seoul, South Korea: Springer, 2007. 828-837 [44] Zhang L, Chu R F, Xiang S M, Liao S C, Li S Z. Face detection based on multi-block LBP representation. In: Proceedings of the 2007 International Conference on Advances in Biometrics. Seoul, South Korea: Springer, 2007. 11-18 [45] Wolf L, Hassner T, Taigman Y. Descriptor based methods in the wild. In: Proceedings of the 2008 European Conference on Computer Vision Workshop on Faces in Real-Life Images. Marseille, France: Springer, 2008. 1-14 [46] Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification. In: Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, USA: IEEE, 1994. 138-142 [47] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720 [48] Phillips P J, Wechsler H, Huang J, Rauss P J. The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing, 1998, 16(5): 295-306 [49] Phillips P J, Flynn P J, Scruggs T, Bowyer K W, Chang J, Hoffman K, Marques J, Jaesik M, Worek W. Overview of the face recognition grand challenge. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 947-954 [50] Huang G B, Ramesh M, Berg T, Learned-Miller E. Labeled Faces in the Wild: a Database for Studying Face Recognition in Unconstrained Environments, Technical Report, Department of Computer Science, University of Massachusetts, USA, 2007 [51] Huang D, Shan C F, Ardabilian M, Wang Y H, Chen L M. Local binary patterns and its application to facial image analysis: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2011, 41(6): 765-781 [52] Chan C H, Kittler J V, Messer K. Multispectral local binary pattern histogram for component-based color face verification. In: Proceedings of the 2007 IEEE Conference on Biometrics: Theory, Applications and Systems. Crystal City, USA: IEEE, 2007. 1-7 [53] Chan C H, Kittler J, Messer K. Multi-scale local binary pattern histograms for face recognition. In: Proceedings of the 2007 International Conference on Biometrics. Seoul, South Korea: Springer, 2007. 809-818 [54] Zhang W C, Shan S G, Gao W, Chen X L, Zhang H M. Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th International Conference on Computer Vision. Beijing, China: IEEE, 2005. 786-791 [55] Tan X Y, Triggs B. Fusing Gabor and LBP feature sets for kernel-based face recognition. In: Proceedings of the 2007 Corference on Analysis and Modeling of Faces and Gestures. Rio de Janeiro, Brazil: Springer, 2007. 235-249 [56] Shan S G, Zhang W C, Su Y, Chen X L, Gao W. Ensemble of piecewise FDA based on spatial histograms of local (Gabor) binary patterns for face recognition. In: Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 606-609 [57] Maturana D, Soto A, Mery D. Face recognition with decision tree-based local binary patterns. In: Proceedings of the 2011 Asian Conference on Computer Vision. Queenstown, New Zealand: Springer, 2011. 618-629 [58] Lahdenoja O, Laiho M, Paasio A. Reducing the feature vector length in local binary pattern based face recognition. In: Proceedings of the 2005 International Conference on Image Processing. Genoa, Italy: IEEE, 2005. 914-917 [59] Zhang B C, Gao Y S, Zhao S Q, Liu J Z. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 2010, 19(2): 533-544 [60] Shan C F, Gritti T. Learning discriminative LBP-histogram bins for facial expression recognition. In: Proceedings of the 2008 British Machine Vision Conference. Leeds, UK: BMVC, 2008. 1-10 [61] Ho An K, Chung M. Cognitive face analysis system for future interactive TV. IEEE Transactions on Consumer Electronics, 2009, 55(4): 2271-2279 [62] Yan S Y, Shan S G, Chen X, Gao W. Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-7 [63] Roy A, Marcel S. Haar local binary pattern feature for fast illumination invariant face detection. In: Proceedings of the 2009 British Machine Vision Conference. London, UK: BMVC, 2009 [64] Yang H, Wang Y D. A LBP-based face recognition method with Hamming distance constraint. In: Proceedings of the 4th International Conference on Image and Graphics. Sichuan, China: IEEE, 2007. 645-649 [65] Fu X F, Wei W. Centralized binary patterns embedded with image Euclidean distance for facial expression recognition. In: Proceedings of the 4th International Conference on Natural Computation. Ji'nan, China: IEEE, 2008. 115-119 [66] Zhang G C, Huang X S, Li S Z, Wang Y S, Wu X H. Boosting local binary pattern (LBP)-based face recognition. In: Proceedings of the 2005 Advances in Biometric Person Authentication. Beijing, China: Springer, 2005. 179-186 [67] Mu Y D, Yan S C, Liu Y, Huang T, Zhou B F. Discriminative local binary patterns for human detection in personal album. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-8 [68] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 886-893 [69] Wang X Y, Han T X, Yan S C. An hog-lbp human detector with partial occlusion handling. In: Proceedings of the 2009 International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 32-39 [70] Trefny J, Matas J. Extended set of local binary patterns for rapid object detection. In: Proceedings of the 2010 Computer Vision Winter Workshop. Nove Hrady, Czech Republic: CVWW, 2010. 1-7 [71] Agarwal S, Awan A, Roth D. Learning to detect objects in images via a sparse, part-based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1475-1490 [72] Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with local binary patterns. Pattern Recognition, 2009, 42(3): 425-436 [73] Zhao G Y, Pietikäinen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 915-928 [74] Kanade T, Cohn J F, Tian Y L. Comprehensive database for facial expression analysis. In: Proceedings of the 4th International Conference on Automatic Face and Gesture Recognition. Grenoble, France: IEEE, 2000. 46-53 [75] Heikkilä M, Pietikäinen M. A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662 [76] Heikkilä M, Pietikäinen M, Heikkilä J. A texture-based method for detecting moving objects. In: Proceedings of the 2004 British Machine Vision Conference. London, UK: BMVC, 2004. 187-196 [77] Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: principles and practice of background maintenance. In: Proceedings of the 1999 IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, 1999. 255-261 [78] Takala V, Pietikäinen, M. Multi-object tracking using color, texture and motion. In: Proceedings of the 2007 IEEE International Workshop on Visual Surveillance. Minneapolis, USA: IEEE, 2007. 1-7 [79] Yao J, Odobez J M. Multi-layer background subtraction based on color and texture. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007. 3658-3665 [80] Liao S C, Zhao G Y, Kellokumpu V, Pietikäinen M, Li S Z. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 1301-1306 [81] Kellokumpu V, Zhao G Y, Pietikäinen M. Human activity recognition using a dynamic texture based method. In: Proceedings of the 2008 British Machine Vision Conference. London, UK: BMVC, 2008. 1-10 [82] Kellokumpu V, Zhao G Y, Pietikäinen M. Texture based description of movements for activity analysis. In: Proceedings of the 2008 International Conference on Computer Vision Theory and Applications. Funchal, Portugal: VISAPP, 2008. 206-213 [83] Kellokumpu V, Zhao G Y, Li S Z, Pietikäinen M. Dynamic texture based gait recognition. In: Proceedings of the 3rd International Conference on Advances in Biometrics. Alghero, Italy: Springer, 2009. 1000-1009 [84] Kellokumpu V, Zhao G Y, Pietikäinen M. Recognition of human actions using texture descriptors. Machine Vision and Applications, 2011, 22(5): 767-780 [85] Costa Y M G, Oliveira L S, Koerich A L, Gouyon F, Martins J G. Music genre classification using LBP textural features. Signal Processing, 2012, 92(11): 2723-2737 [86] Caputo B, Hayman E, Mallikarjuna P. Class-specific material categorisation. In: Proceedings of the 2005 IEEE International Conference on Computer Vision. Beijing, China: IEEE, 2005. 1597-1604 [87] Caputo B, Hayman E, Fritz M, Eklundh J O. Classifying materials in the real world. Image and Vision Computing, 2010, 28(1): 150-163
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