Scale invariant texture classification via sparse representation

Scale change exists very commonly in real-world textural images which remains one of the biggest challenges in texture classification due to the tremendous changes involved in texture appearance. While most research efforts have been devoted to extracting various scale invariant features, these methods are either unsuitable to describe a texture or unable to handle the situations where a large amount of scale change exists. Other works attempt to avoid scale invariant feature extraction by generating a set of multi-scale representations from training images for classification, but they are not only computation intensive but also limited to dealing with small scale changes between training images and test images. In this paper we investigate the scaling properties of textures and introduce a low dimensional linear subspace for the multi-scale representations of a texture, in which the collaboration between the multi-scale representations is beneficial for the scale invariant texture classification. We therefore propose a new scale invariant texture classification framework without extracting scale invariant features, by using a sparse representation technique to model the multi-scale representations of a texture and taking the advantages of collaboration between them for classification. Specifically, a multi-scale dictionary is constructed from the Gaussian-pyramid-generated scale space of a small set of training images at one scale, and then the test images at arbitrary scales are classified via a modified sparse representation based classification method. Experiments on two benchmark texture databases show that the proposed method is able to deal with large scale changes between the training images and the test images and achieve comparative results to the state-of-the-art approaches for the classification of textures with various variations, especially scale.