LBP histogram selection for supervised color texture classification

In this paper, we propose a Local Binary Pattern (LBP) histogram selection approach. It consists in assigning to each histogram a score which measures its efficiency to characterize the similarity of the textures within the different classes. The histograms are then ranked according to the proposed score and the most discriminant ones are selected. Experiments, which have been carried out on benchmark color texture image databases, show that the proposed histogram selection approach is able to improve the classification performances.