A multi-channel framework based Local Binary Pattern with two novel local feature descriptors for texture classification

Local binary pattern (LBP) has been widely used in various application fields, including object detection, texture analysis, and remote sensing. To improve discrimination performance, many LBP-based methods have been proposed for extracting different local feature information for texture classification. However, current LBP-based algorithms typically describe local features at a single sampling scale, but some significant and discriminative texture feature information is contained between different scales. Therefore, the lack of capability to extract cross-scale features can lead to losing critical texture features. Moreover, low-frequency texture information should be accorded great importance in texture classification. Additionally, if the diversity and validity of local feature extraction are lacking, the effectiveness of classification capability suffers. To address these issues, (1) we propose a completed cross-scale Local binary pattern (ccsLBP) operator to extract cross-scale texture features. (2) A mean-filtered Local binary pattern (LBP^mf) operator is presented to highlight the important low-frequency texture information of texture images. (3) We build a high-performing multi-channel framework based Local binary pattern (MC-LBP) for texture classification, which combines complementary features extracted by LBP, ccsLBP, and LBP^mf hybridly to form a final feature vector of the texture image. The effectiveness of the proposed MC-LBP framework is verified on 6 representative texture databases, and the experimental results demonstrate its state-of-art texture classification performance.