Extended complete local binary pattern for texture classification

In this paper, an extended complete LBP (ELBP) for texture classification is proposed, in which the local feature vectors are composed of the ratio of the central pixel and its neighborhood pixels to a specific threshold. ECLBP_C represents the gray level of the image, which is obtained by comparing the center pixel with the global threshold. ECLBP_S and ECLBP_M represent the symbol component and the magnitude component of the 3-neighbor region of the center pixel respectively, which are obtained by calculating two binary codes using the original LBP algorithm for the 3-neighbor region of the center pixel. In order to make the proposed algorithm scalable, in addition to the 3-neighbor pixels of the central pixel, the proposed algorithm use the center pixel as the center, r as the radius in the circle with ɑ as the filter’s radius to generate extended binary coding, such as ECLBP_ES_r,α ECLBP_EM_ r,α. In order to describe the local region feature vector in detail, specified ECLBP_ES_r,α and ECLBP_EM_r,α can be obtained by defining the number of extensions according to actual needs, and then established and concatenated all ECLBP gray histograms for statistics. In the experimental part, we analyze the performance of the proposed algorithm in detail, and prove that the algorithm has good scalability and robustness. The experimental results show that the classification accuracy of the proposed algorithm is up to 99% after 3 expansions in Table 2. The source codes of the proposed algorithm can be downloaded from https://github.com/zenqiang/ECLBP.git.