An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform

The M-band wavelet decomposition, which is a direct generalization of the standard 2-band wavelet decomposition is applied to the problem of an unsupervised segmentation of two texture images. Orthogonal and linear phase Ill-band wavelet transform is used to decompose the image into M x M channels. Various combinations of these bandpass sections are taken to obtain different scales and orientations in the frequency plane. Texture features are obtained by subjecting each bandpass section to a nonlinear transformation and computing the measure of energy in a window around each pixel of the filtered texture images. The window size in turn is adaptively selected depending on the frequency content of the images. Unsupervised texture segmentation is obtained by simple K-means clustering. Statistical tests are used to evaluate the average performance of features extracted from the decomposed subbands. (C) 2001 Elsevier Science B.V. All rights reserved.