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A distinct and compact texture descriptor
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DOI：10.1016/j.imavis.2014.02.004.png)
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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. (C) 2014 Elsevier BM. All rights reserved.
Keyword:
Texture description
Local binary pattern
Fractal dimension
Multi-fractal analysis
Texture classification
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