An efficient and effective texture classification approach using a new notion in wavelet theory

This paper presents a novel multiresolution approach to the classification of textures using wavelets. The approach uses an overcomplete wavelet decomposition, called wavelet-frames, which yields the descriptions of both translation invariance and stability. In order to adapt it to the quasi-periodic properly of textures, we first detect the channels containing dominant information, and then zoom it into these frequency channels for further decomposition. For classification efficiency, we develop a progressive texture classification algorithm, in which the classification process terminates once a suitably chosen discrimination criterion is met. Experiments show that with a minimum number of wavelet frame decompositions and iterations, our proposed approach achieves a 100% correct classification rate on all the texture types tested. It outperforms many of the existing approaches in terms of classification excellence and computational efficiency, and hence appears attractive for real-time applications involving texture-based video/image classification.