  To overcome the limitations of original local binary patterns (LBP), this
article proposes a new texture descriptor aided by complex networks (CN) and
LBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as
directed graphs over different bands with the help of pixel distance,
intensity, and gradient (magnitude and angle). Second, several CN-based feature
measurements (including clustering coefficient, in-degree centrality,
out-degree centrality, and eigenvector centrality) are selected to further
decipher the texture features, which generates four feature images that can
retain the image information as much as possible. Third, given the original
TIs, gradient images (GI), and generated feature images, we can obtain the
discriminative representation of texture images based on uniform LBP (ULBP).
Finally, the feature vector 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
resistance to imaging and noise. Experiment results on four datasets
demonstrate that the proposed texture descriptor can significantly improve the
classification accuracies compared with the state-of-the-art LBP-based variants
and deep learning-based methods.
