Adaptive basis selection for multi texture segmentation by M-band wavelet packet frames

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