Color Texture Description Based on Holistic and Hierarchical Order-Encoding Patterns
Tiecheng Song, Jie Feng, Yuanlin Wang, Chenqiang Gao · 2021
Local binary pattern (LBP), as one of the most representative texture operators, has attracted much attention in computer vision and pattern recognition. Many LBP variants were developed in the literature. However, most of them were designed for gray images and their performance remains to be improved for color images. In this paper, we propose a novel color image descriptor named Holistic and Hierarchical Order-Encoding Patterns (H2OEP) for texture classification. In H2OEP, the holistic order-encoding pattern compactly encodes color order variation tendencies for each pixel in color space. The hierarchical order-encoding pattern leverages min ordering, median ordering and max ordering to encode local neighboring relationships across different color channels. Finally, the generated order-encoding patterns are aggregated via central pixel encoding to build 3D joint histograms for image representation. Experiments on four benchmark texture databases demonstrate the effectiveness of the proposed descriptor for color texture classification.