Sparseness-Based Descriptors for Texture Segmentation

This paper exploits the concept of sparseness to generate novel contextual multi-resolution texture descriptors. We propose to extract low-dimension features from Gabor-filtered images by considering the sparseness of filter bank responses. We construct several texture descriptors: the basic version describes each pixel by its contextual textural sparseness, while other versions also integrate multi-resolution information. We apply the novel low-dimension sparseness-based descriptors to the problem of texture segmentation and evaluate their performance on the public Outex database. The sparseness-based descriptors show a substantial improvement over Gabor filters with respect not only to computational costs and memory usage, but also to segmentation accuracy. The proposed approach also shows a desirable smooth, monotonic behavior with respect to the dimensionality of the descriptors.