Maximum likelihood texture classification and Bayesian texture segmentation using discrete wavelet frames

Abstract

A new approach is presented for the classification and segmentation of texture images, where a different statistical methodology and criterion for texture characterization is proposed. The scheme, in both problems, uses the concept of discrete wavelet frames for the appropriate frequency decompositions, as applied to 2-D signals, and a distance measure based on the evaluation of parametric scatter matrices of the texture images to be segmented or classified. Experiments yielding excellent results are presented for both algorithms.