Structured Textons for texture representation

In this paper, we propose a novel texture descriptor, Structured Texton, to extract and characterize meaningful texture patterns in images. Structured Textons are constructed by grouping local extremum regions connected by the nesting relationship. To further improve the discriminative ability, high order texton words are generated from the Structured Textons, preserving both the appearance information and the spatial information. Finally, a semantic ranking criterion is proposed for selecting the discriminative high order texton words by means of finding informative patterns from images. The proposed Structured Texton is more discriminative than the single texton-based representation. Experimental results of texture classification and scene classification on public datasets demonstrate the effectiveness and discrimination of the proposed Structured Texton.