Local fractal dimension and binary patterns in texture recognition

• A new approach is proposed to extract features from grey-level texture images.
• Based on computing binary patterns from the local fractal dimension.
• The method was applied to the classification of two well-known benchmark databases.
• The proposal outperformed other state-of-the-art and classical approaches.
• The new descriptors are potentially useful for general purpose applications.

The present work proposes a new texture image descriptor, combining the local binary patterns extracted from the grey-level image (classic approach) with those extracted from the local fractal dimension at each point of the image. In this way, these descriptors express two important measurements from the image, i.e., the variation among pixel intensities in each local neighbourhood and the local complexity (pixel arrangement) at each point. Such combination provides a rich and robust descriptor even for the most complex textures. The effectiveness of the proposed solution is evaluated in the classification of two well-known benchmark databases: UIUC and USPTex, showing that the combined features outperform all the other compared approaches in terms of correctness rates in the classification of grey-scale texture images.