Attractive-and-repulsive center-symmetric local binary patterns for texture classification

Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling o the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageou characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texturi modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the cente pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizonta directions, and the two diagonal directions by including the value of the central pixel in the modeling. addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value ove 3 x 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. T( capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust ant stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in method based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiment performed on thirteen challenging representative texture databases show that the proposed operators can achievi impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number o recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accurac' improvement is demonstrated through Wilcoxon signed rank test.