Exploring Cross-Channel Texture Correlation for Color Texture Classification

This paper proposes a novel approach to encode cross-channel texture correlation for color texture classification task. Firstly, we quantitatively study the correlation between different color channels using Local Binary Pattern (LBP) as the texture descriptor and using Shannon’s information theory to measure the correlation. We find that (R, G) channel pair exhibits stronger correlation than (R, B) and (G, B) channel pairs. Secondly, we propose a novel descriptor to encode the cross-channel texture correlation. The proposed descriptor can capture well the relative variance of texture patterns between different channels. Meanwhile, our descriptor is computationally efficient and robust to image rotation. We conduct extensive experiments on four challenging color texture databases to validate the effectiveness of the proposed approach. The experimental results show that the proposed approach significantly outperforms its mostly relevant counterpart (Multichannel color LBP), and achieves the state-of-the-art performance.