CFA local binary patterns for fast illuminant-invariant color texture classification

This paper focuses on the classification of color textures acquired by single-sensor color cameras under various illuminants. Local binary patterns (LBPs) are robust texture descriptors suited to such conditions. This property is still improved when LBPs are computed from the level ranks. Our main contribution is to avoid the demosaicing step that is classically performed in single-sensor color cameras to estimate color images from raw data. We instead compute rank-based LBPs from the color filter array image, in which each pixel is associated to a single color component. Experimental results achieved on a benchmark color texture database show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency.

[1]  Paul S. Heckbert.  Color image quantization for frame buffer display , 1998 .

[2]  Matti Pietikäinen,et al.  Color Texture Classification with Color Histograms and Local Binary Patterns , 2002 .

[3]  Kurt Konolige,et al.  Fast color/texture segmentation for outdoor robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Yap-Peng Tan,et al.  Adaptive Filtering for Color Filter Array Demosaicking , 2007, IEEE Transactions on Image Processing.

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

[6]  Matti Pietikäinen,et al.  Classification with color and texture: jointly or separately? , 2004, Pattern Recognit..

[7]  Liming Chen,et al.  Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[8]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yong Man Ro,et al.  Color Local Texture Features for Color Face Recognition , 2012, IEEE Transactions on Image Processing.

[10]  GeversTheo,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010 .

[11]  Matti Pietikäinen,et al.  Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[13]  Wen-Hung Liao.  Region Description Using Extended Local Ternary Patterns , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  Matti Pietikäinen,et al.  Optimising Colour and Texture Features for Real-time Visual Inspection , 2002, Pattern Analysis & Applications.

[15]  Rastislav Lukac,et al.  Single-sensor imaging in consumer digital cameras: a survey of recent advances and future directions , 2006, Journal of Real-Time Image Processing.

[16]  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.

[17]  Si-Yu Xia,et al.  Illumination invariant face recognition based on improved Local Binary Pattern , 2011, Proceedings of the 30th Chinese Control Conference.

[18]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Paul Southam,et al.  Theoretical and experimental comparison of different approaches for color texture classification , 2011, J. Electronic Imaging.

[20]  Ludovic Macaire,et al.  Histogram of fuzzy ranks for object recognition across illumination changes , 2006, CGIV.

[21]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.

[22]  L. Macaire,et al.  Comparison of color demosaicing methods , 2010 .

[23]  Giancarlo Calvagno,et al.  Color image demosaicking: An overview , 2011, Signal Process. Image Commun..

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

[25]  Gerald Schaefer,et al.  Illuminant and device invariant colour using histogram equalisation , 2005, Pattern Recognit..

[26]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[28]  Ludovic Macaire,et al.  Fuzzy Spatial Ranks for Object Recognition Across Illumination Changes , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[29]  Yang Zhao.  Theories and Applications of LBP: A Survey , 2011, ICIC.

[30]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[31]  L. Macaire,et al.  Haralick feature extraction from LBP images for color texture classification , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[32]  Yoshikatsu Kimura,et al.  Texture-Based Objects Recognition for Vehicle Environment Perception Using a Multiband Camera , 2007, ISVC.

[33]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[34]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

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

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