Texture description through histograms of equivalent patterns

The aim of this paper is to describe a general framework for texture analysis which we refer to as the HEP (histograms of equivalent patterns). The HEP, of which we give a clear and unambiguous mathematical definition, is based on partitioning the feature space associated to image patches of predefined shape and size. This task is approached by defining, a priori, suitable local or global functions of the pixels' intensities. In a comprehensive survey we show that diverse texture descriptors, such as co-occurrence matrices, gray-level differences and local binary patterns, can be seen all to be examples of the HEP. In the experimental part we comparatively evaluate a comprehensive set of these descriptors on an extensive texture classification task. Within the class of HEP schemes, improved local ternary patterns (ILTP) and completed local binary patterns (CLBP) emerge as the best of parametric and non-parametric methods, respectively. The results also show the following patterns: 1) higher effectiveness of multi-level discretization in comparison with binarization; 2) higher accuracy of parametric methods when compared to non-parametric ones; 3) a general trend of increasing performance with increasing dimensionality; and 4) better performance of point-to-average thresholding against point-to-point thresholding.