A completed region contrast binary pattern for texture image classification
Xiaochun Xu, Tong Xiao · 2024
Texture is a key and basic visual cue for various image processing applications. To capture rich and discriminative local texture information, this paper develops a completed region contrast binary pattern (CRCBP), which introduces a novel pixel-region contrast measure scheme to extract contrast difference information between the pixel and its local background region. The proposed CRCBP consists of three components: local difference sign, local difference magnitude, and local pixel-region contrast measure sign. According to the local neighborhood encoding mechanism, we can get three sub-patterns: sign binary pattern, magnitude binary pattern, and local region contrast binary pattern. To achieve completed texture feature representation, these three sub-patterns are jointly combined to obtain the completed region contrast binary pattern. The comparative evaluations on benchmark database shows that the proposed CRCBP descriptor yields the state-of-the-art classification performance.