In this paper, we propose a scheme for segmentation of multitexture images. The methodology involves extraction of texture features using an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DMbWPF). This is followed by the selection of important features using a neuro-fuzzy algorithm under unsupervised learning. A computationally efficient search procedure is developed for finding the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters for each of the subbands. The superior discriminating capability of the extracted features for segmentation of various texture images over those obtained by several existing methods is established.

The present paper describes a feature extraction method based on M-band wavelet packet frames for segmenting remotely sensed images. These wavelet features are then evaluated and selected using an efficient neurofuzzy algorithm. Both the feature extraction and neurofuzzy feature evaluation methods are unsupervised, and they do not require the knowledge of the number and distribution of classes corresponding to various land covers in remotely sensed images. The effectiveness of the methodology is demonstrated on two four-band Indian Remote Sensing 1A satellite (IRS-1A) images containing five to six overlapping classes and a three-band SPOT image containing seven overlapping classes.

An efficient and computationally fast method for segmenting text and graphics part of document images based on textural cues is presented. We assume that the graphics part have different textural properties than the nongraphics (text) part. The segmentation method uses the notion of multiscale wavelet analysis and statistical pattern recognition. We have used M-band wavelets which decompose an image into M/spl times/M bandpass channels. Various combinations of these channels represent the image at different scales and orientations in the frequency plane. The objective is to transform the edges between textures into detectable discontinuities and create the feature maps which give a measure of the local energy around each pixel at different scales. From these feature maps, a scale-space signature is derived, which is the vector of features at different scales taken at each single pixel in an image. We achieve segmentation by simple analysis of the scale-space signature with traditional k- means clustering. We do not assume any a priori information regarding the font size, scanning resolution, type of layout, etc. of the document in our segmentation scheme.

We propose an approach for texture feature extraction based on M-band wavelet packet frames. The features so extracted are used for segmentation of multi texture images. Standard dyadic wavelets are not suitable for the analysis of high frequency signals with relatively narrow bandwidth and also are not translation invariant. Also, since most significant information of a texture often lies in the intermediate frequency bands, the present work employs an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DM-bWPF), which yields improved segmentation accuracies. Wavelet packets represent a generalization of the method of multiresolution decomposition and comprise all possible combinations of subband tree decomposition. We propose a computationally efficient search procedure to find the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters of each of the subbands, to locate dominant information in each subband (frequency channel) and decide further decomposition.

The M-band wavelet decomposition, which is a direct generalization of the standard 2-band wavelet decomposition has been applied to the problem of an unsupervised segmentation of two texture systems. Standard wavelets are not suitable for the analysis of high frequency signals with relatively narrow bandwidth. So we propose to use the decomposition scheme based on M-band wavelets, that yield improved segmentation accuracies. Unlike the standard wavelet decomposition which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Further motivation to use the M-band wavelet filter for texture analysis is because this decomposition yields a large number of sub-bands which is required for good quality segmentation.