A Comparison of Image Texture Descriptors for Pattern Classification

Texture classification is a problem widely studied in computer vision, there exist two fundamental issues: how to describe texture images and how to define a similarity measure. The texture descriptors are mainly used to extract and represent the features of texture images and their performance is usually measured using a classification algorithm. In this paper, some of the most referenced texture descriptors, such as Gabor filter banks, Wavelets, and Local Binary Patterns, are compared using non-parametric statistical tests to know if there is a difference in performance. The descriptors are applied to five well-known texture image datasets, in order to be classified. Three classification algorithms, with a cross-validation scheme, are used to classify the described texture datasets. Finally, a Friedman test with multiple comparisons is used to compare the whole performance of the texture descriptors on a statistical basis. The statistical results suggest that for these tests there is a difference in performance, so it was possible to determine statistically, for the considered experimental settings, the best texture descriptor.