Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification

This paper presents new modeling of local binary patterns for texture representation. Referred to as local binary gradient contours (LBGC), the proposed models are expected to better represent the salient local texture structure. Thanks to the flexibility of repulsive-attractive characteristics, which represent the cornerstone of the proposed descriptors, two distinct LBP-like descriptors are built: repulsive and attractive local binary gradient contours (RLBGC and ALBGC). Conventional methods such as LBP, the family of binary gradient contours (BGC1, BGC2 and BGC3), LBP by neighborhoods (nLBPd) and several other LBP-like methods, are based on pairwise comparison of adjacent pixels. Unlike these methods, the proposed RLBGC and ALBGC operators encode the differences between local intensity values within triplets of pixels, along a closed path around the central pixel of a 3x3 gray-scale image patch. In order to increase the robustness of the proposed RLBGC and ALBGC descriptors, the triplet formed by the average local and average global gray levels (ALGL and AGGL) and the central pixel is incorporated in the modeling. In order to make the proposed approach more robust and stable, the RLBGC and ALBGC are concatenated together to form multi-scale repulsive-and-attractive local binary gradient contour (RALBGC) descriptor. Extensive experimental results from 13 challenging representative texture datasets show that the proposed descriptors, applied on each dataset, can achieve interesting classification accuracy, which is competitive or better than a great number of state-of-the-art LBP variants and non-LBP methods. Furthermore, statistical hypothesis testing is performed to prove the statistical significance of the achieved accuracy improvement over all the tested datasets.