Texture analysis by multi-resolution fractal descriptors

Joao B. Florindo and Odemir M. Bruno

EXPERT SYSTEMS WITH APPLICATIONS, 40(10):4022-4028, 2013

This work proposes a novel texture descriptor based on fractal theory. The method is based on the Bouligand-Minkowski descriptors. We decompose the original image recursively into four equal parts. In each recursion step, we estimate the average and the deviation of the Bouligand-Minkowski descriptors computed over each part. Thus, we extract entropy features from both average and deviation. The proposed descriptors are provided by concatenating such measures. The method is tested in a classification experiment under well known datasets, that is, Brodatz and Vistex. The results demonstrate that the novel technique achieves better results than classical and state-of-the-art texture descriptors, such as Local Binary Patterns, Gabor-wavelets and co-occurrence matrix. (C) 2013 Published by Elsevier Ltd.