CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification

To effectively overcome the limitations of local binary patterns (LBP), this letter proposes a new texture descriptor aided by complex networks (CN) and uniform LBP (ULBP), namely, CN-LBP. Specifically, we first abstract a gray-scale image (GSI) as an undirected graph with the help of pixel distance and intensity, and gradient of image (GoI). Second, three variants of CN-based feature measurements (clustering coefficient, degree centrality, and eigenvector centrality) are proposed to decipher the image spatial-relationship, energy, and entropy, respectively, thus generating three feature maps, which can retain the image information as much as possible. Third, given the generated feature maps, we apply ULBP on feature maps, GSI, and GoI, and obtain the discriminative representation of the texture image. Finally, CN-LBP is obtained by jointly calculating and concatenating the spatial histograms. In contrast to original LBP, the proposed texture descriptor contains more detailed image information and shows certain resistance to noise. Experiment results show that the proposed approach significantly improves the texture classification accuracy compared with state-of-the-art LBP-based variants and deep learning-based approaches.