Texture discrimination using wavelets

A new approach to the characterization of texture properties at multiple scales using an overcomplete wavelet transform is described. It is shown that this representation constitutes a tight frame of l₂, and that it has a fast iterative algorithm. A texture is characterized by a set of channel variances estimated at the output of the corresponding filter-bank. Classification experiments with 12 Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction. This result also suggests that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, etc. . .). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is provided. The DWF feature extraction technique is incorporated into a simple multiple-component texture segmentation algorithm. Some examples are presented