Robust Texture Description Using Local Grouped Order Pattern and Non-Local Binary Pattern

Local binary pattern (LBP) and its many variants have shown effectiveness for texture classification. However, most of these LBP methods focus on encoding local intensity differences between a central pixel and its neighboring sampling points and consequently have two major problems: 1) they are unable to describe the intensity order relationships among neighboring sampling points, and 2) they fail to capture long-range pixel interactions that take place outside a compact neighborhood. In view of these problems, in this paper we propose two novel operators, called local grouped order pattern (LGOP) and non-local binary pattern (NLBP), for texture description. For the first problem, LGOP groups the neighboring sampling points by referring to a dominant direction and encodes the groupwise intensity order relationships. For the second problem, NLBP computes several anchors based on global image statistics and progressively encodes non-local intensity differences between the neighboring sampling points and anchors. Finally, we combine LGOP and NLBP via central pixel encoding to construct discriminative histogram features as texture descriptor LGONBP. Experiments on four texture benchmark databases (i.e., Outex, CUReT, UMD and KTH-TIPS) demonstrate the superiority of LGONBP over state-of-the-art LBP variants for texture classification under both noise-free and noisy conditions. The code is available at https://github.com/stc-cqupt/LGONBP.