Discrete wavelet transform with optimal joint localization for determining the number of image texture segments

Accurate estimation of the number of textured regions that are present in an image is one of the most difficult aspects of the unsupervised texture segmentation problem. In this paper we introduce a new approach for estimating the number of regions in an image without a priori information. Using a novel discrete-discrete uncertainty measure defined on equivalence classes of signals, we design a localized separable 2-D wavelet transform. By clustering in a feature space defined by the wavelet coefficients computed over disjoint blocks in the image, we obtain high quality estimates for the number of textured regions present in an image. Compared to a previously reported algorithm based on the eight-point Daubechies wavelet, this new approach tends to produce clusters with improved between-cluster separations.