Noise-Robust Texture Description Using Local Contrast Patterns via Global Measures

This letter presents a noise-robust descriptor by exploring a set of local contrast patterns (LCPs) via global measures for texture classification. To handle image noise, the directed and undirected difference masks are designed to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. To describe pixel-wise features, these responses are separately quantized and encoded into specific patterns based on different global measures. These resulting patterns (i.e., LCPs) are jointly encoded to form our final texture representation. Experiments are conducted on the well-known Outex and CUReT databases in the presence of high levels of noise. Compared to many state-of-the-art methods, the proposed descriptor achieves superior texture classification performance while enjoying a compact feature representation.

[1]  Fakhry M. Khellah,et al.  Texture Classification Using Dominant Neighborhood Structure , 2011, IEEE Transactions on Image Processing.

[2]  Hongliang Li,et al.  WaveLBP based hierarchical features for image classification , 2013, Pattern Recognit. Lett..

[3]  Xudong Jiang,et al.  Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism , 2013, IEEE Transactions on Image Processing.

[4]  Renato Campanini,et al.  Texture classification using invariant ranklet features , 2008, Pattern Recognit. Lett..

[5]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.

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

[7]  Fabrizio Smeraldi.  Ranklets: orientation selective non-parametric features applied to face detection , 2002, Object recognition supported by user interaction for service robots.

[8]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

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

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Xin Yang,et al.  Rotation Invariant Texture Descriptor Using Local Shearlet-Based Energy Histograms , 2013, IEEE Signal Processing Letters.

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

[13]  Francesco Bianconi,et al.  Robust color texture features based on ranklets and discrete Fourier transform , 2009, J. Electronic Imaging.

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

[15]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[17]  Hongliang Li,et al.  Local Polar DCT Features for Image Description , 2013, IEEE Signal Processing Letters.

[18]  King Ngi Ngan,et al.  Saliency model-based face segmentation and tracking in head-and-shoulder video sequences , 2008, J. Vis. Commun. Image Represent..