A distinct and compact texture descriptor

•A powerful texture descriptor is developed for texture classification.•The descriptor is built via fractal analysis on the local binary patterns.•The descriptor enjoys both high discriminative power and robustness.•The descriptor is compact and computationally efficient.•The descriptor demonstrated excellent performance on four datasets.

In this paper, a statistical approach to static texture description is developed, which combines a local pattern coding strategy with a robust global descriptor to achieve highly discriminative power, invariance to photometric transformation and strong robustness against geometric changes. Built upon the local binary patterns that are encoded at multiple scales, a statistical descriptor, called pattern fractal spectrum, characterizes the self-similar behavior of the local pattern distributions by calculating fractal dimension on each type of pattern. Compared with other fractal-based approaches, the proposed descriptor is compact, highly distinctive and computationally efficient. We applied the descriptor to texture classification. Our method has demonstrated excellent performance in comparison with state-of-the-art approaches on four challenging benchmark datasets.