A Unifying Framework for LBP and Related Methods

In this chapter we describe a unifying framework for local binary patterns and variants which we refer to as histograms of equivalent patterns (HEP). In presenting this concept we discuss some basic issues in texture analysis: the problem of defining what texture is; the problem of classifying the many existing texture descriptors; the concept of bag-of-features and the design choices that one has to deal with when designing a texture descriptor. We show how this relates to local binary patterns and related methods and propose a unifying mathematical formalism to express them within the HEP. Finally, we give a geometrical interpretation of these methods as partitioning operators in a high-dimensional space, showing how this representation can propound possible directions for future research.

[1]  Eduard Montseny,et al.  On the Fuzzy Texture Spectrum for Natural Microtextures Characterization , 2005, EUSFLAT Conf..

[2]  G. Stachowiak,et al.  A Comparison of Texture Feature Extraction Methods for Machine Condition Monitoring and Failure Analysis , 2005 .

[3]  Manuel G. Penedo,et al.  Texture description in local scale using texton histograms with quadrature filter universal dictionaries , 2011 .

[4]  Nong Sang,et al.  Robust Illumination Invariant Texture Classification Using Gradient Local Binary Patterns , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[5]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[6]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Ricardo da Silva Torres,et al.  Comparative study of global color and texture descriptors for web image retrieval , 2012, J. Vis. Commun. Image Represent..

[8]  Francesco Bianconi,et al.  Evaluation of the effects of Gabor filter parameters on texture classification , 2007, Pattern Recognit..

[9]  Chin-Wang Tao,et al.  Texture classification using a fuzzy texture spectrum and neural networks , 1998, J. Electronic Imaging.

[10]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[11]  Chong-Wah Ngo,et al.  Towards optimal bag-of-features for object categorization and semantic video retrieval , 2007, CIVR '07.

[12]  T. J. Stonham,et al.  A single layer neural network for texture discrimination , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.

[13]  Wei Wei,et al.  Centralized Binary Patterns Embedded with Image Euclidean Distance for Facial Expression Recognition , 2008, 2008 Fourth International Conference on Natural Computation.

[14]  Maria Petrou,et al.  Image processing - dealing with texture , 2020 .

[15]  B Julesz,et al.  Experiments in the visual perception of texture. , 1975, Scientific American.

[16]  Harry Wechsler,et al.  Texture analysis — a survey , 1980 .

[17]  J. Lawrence Polytope volume computation , 1991 .

[18]  Francesco Bianconi,et al.  Texture Classification Through Combination of Sequential Colour Texture Classifiers , 2007, CIARP.

[19]  Loris Nanni,et al.  A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states , 2010, Expert Syst. Appl..

[20]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[21]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[22]  Lei Wang,et al.  In defense of soft-assignment coding , 2011, 2011 International Conference on Computer Vision.

[23]  M. Pietikäinen,et al.  TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS , 2004 .

[24]  Emanuele Della Valle,et al.  An Introduction to Information Retrieval , 2013 .

[25]  Francesco Bianconi,et al.  Image classification with binary gradient contours , 2011 .

[26]  Matti Pietikäinen,et al.  Computer Vision Using Local Binary Patterns , 2011, Computational Imaging and Vision.

[27]  Paul F. Whelan,et al.  Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification , 2011, Machine Vision and Applications.

[28]  Antonio Fernández,et al.  One-class texture classifier in the CCR feature space , 2003, Pattern Recognit. Lett..

[29]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[30]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.

[31]  Mohamed S. Kamel,et al.  Dictionary Learning in Texture Classification , 2011, ICIAR.

[32]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[33]  D. Geman,et al.  Invariant Statistics and Coding of Natural Microimages , 1998 .

[34]  T. Ojala,et al.  Gray level cooccurrence histograms via learning vector quantization , 1999 .

[35]  Dong-Chen He,et al.  Texture Unit, Texture Spectrum, And Texture Analysis , 1990 .

[36]  Andrew Zisserman,et al.  Unifying statistical texture classification frameworks , 2004, Image Vis. Comput..

[37]  Erkki Oja,et al.  Reduced Multidimensional Co-Occurrence Histograms in Texture Classification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Changxin Gao,et al.  Multi-structure local binary patterns for texture classification , 2011, Pattern Analysis and Applications.

[39]  Paul F. Whelan,et al.  Local binary patterns versus signal processing texture analysis: a study from a performance evaluati , 2012 .

[40]  Francesco Bianconi,et al.  Rotation-invariant colour texture classification through multilayer CCR , 2009, Pattern Recognit. Lett..

[41]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[42]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[43]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  S. Robins,et al.  Computing the Continuous Discretely , 2015 .

[45]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[46]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[47]  Alan L. Yuille,et al.  Statistical cues for domain specific image segmentation with performance analysis , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[48]  M. Pietikäinen,et al.  SOFT HISTOGRAMS FOR LOCAL BINARY PATTERNS , 2007 .

[49]  Dimitrios K. Iakovidis,et al.  Fuzzy Local Binary Patterns for Ultrasound Texture Characterization , 2008, ICIAR.

[50]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[51]  George Economou,et al.  Distributional-based texture classification using non-parametric statistics , 2008, Pattern Analysis and Applications.

[52]  Dong-Chen He,et al.  Unsupervised textural classification of images using the texture spectrum , 1992, Pattern Recognit..

[53]  Loris Nanni,et al.  Random interest regions for object recognition based on texture descriptors and bag of features , 2012, Expert Syst. Appl..

[54]  Bertrand Zavidovique,et al.  Median Binary Pattern for Textures Classification , 2007, ICIAR.

[55]  Francisco José Madrid-Cuevas,et al.  Simplified Texture Unit: A New Descriptor of the Local Texture in Gray-Level Images , 2003, IbPRIA.

[56]  E. R. Davies Introduction to Texture Analysis , 2008 .

[57]  Farshad Tajeri pour,et al.  Texture classification approach based on combination of random threshold vector technique and co-occurrence matrixes , 2011, Proceedings of 2011 International Conference on Computer Science and Network Technology.

[58]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[60]  M. Unser Local linear transforms for texture measurements , 1986 .

[61]  Bin Fan,et al.  Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.

[62]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[63]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[64]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[65]  Xianghua Xie,et al.  A Galaxy of Texture Features , 2008 .

[66]  Marcos X. Álvarez-Cid,et al.  Texture Description Through Histograms of Equivalent Patterns , 2012, Journal of Mathematical Imaging and Vision.

[67]  Hui Zhang,et al.  Local image representations using pruned salient points with applications to CBIR , 2006, MM '06.

[68]  DeLiang Wang,et al.  Texture classification using spectral histograms , 2003, IEEE Trans. Image Process..

[69]  Lewis D. Griffin,et al.  Using Basic Image Features for Texture Classification , 2010, International Journal of Computer Vision.

[70]  J. M. D. Meiklejohn,et al.  The Critique of Pure Reason , 2020, A Commentary on Kant’s Critique of Judgment.

[71]  Cheng Wang,et al.  A novel extended local-binary-pattern operator for texture analysis , 2008, Inf. Sci..

[72]  Hanqing Lu,et al.  Face detection using improved LBP under Bayesian framework , 2004, Third International Conference on Image and Graphics (ICIG'04).

[73]  Paul W. Fieguth,et al.  Extended local binary patterns for texture classification , 2012, Image Vis. Comput..

[74]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[75]  Chein-I. Chang,et al.  Gradient texture unit coding for texture analysis , 2004 .

[76]  David Zhang,et al.  Texture classification via patch-based sparse texton learning , 2010, 2010 IEEE International Conference on Image Processing.

[77]  T. J. Stonham,et al.  Texture image classification and segmentation using RANK-order clustering , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[78]  Nicu Sebe,et al.  Texture Features for Content-Based Retrieval , 2001, Principles of Visual Information Retrieval.

[79]  Andrew Zisserman,et al.  Texture classification: are filter banks necessary? , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[80]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[81]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[82]  José Francisco Martínez-Trinidad,et al.  Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007, Proceedings , 2008, CIARP.

[83]  B. Xu,et al.  Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image , 2003 .

[84]  Yung-Chang Chen,et al.  Statistical feature matrix for texture analysis , 1992, CVGIP Graph. Model. Image Process..

[85]  Francesco Bianconi,et al.  On the Occurrence Probability of Local Binary Patterns: A Theoretical Study , 2011, Journal of Mathematical Imaging and Vision.

[86]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[87]  Alberto Sanfeliu,et al.  Signatures versus histograms: Definitions, distances and algorithms , 2006, Pattern Recognit..

[88]  Cor J. Veenman,et al.  Visual Word Ambiguity , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[89]  M. Cervantes,et al.  Quasi-statistical approach to digital binary image representation , 1996 .

[90]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[91]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[92]  T. John Stonham,et al.  Texture classification using n-tuple pattern recognition , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[93]  Frédéric Jurie,et al.  Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[94]  Paul W. Fieguth,et al.  Texture Classification from Random Features , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[95]  S. Robins,et al.  Computing the Continuous Discretely: Integer-Point Enumeration in Polyhedra , 2007 .

[96]  Cordelia Schmid,et al.  Vector Quantizing Feature Space with a Regular Lattice , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[97]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).