Texture classification and segmentation using incomplete tree structured wavelet packet frame and Gaussian mixture model

In this paper, we propose a scheme for texture classification and segmentation. The methodology involves extraction of energy as a texture feature using incomplete tree-structured wavelet packet decomposition. This is followed by a Gaussian-mixture-based classifier, which assigns each pixel to the class. Each subnet of the classifier is modeled by a Gaussian mixture model. Each texture image is assigned to the class to which pixels of the image most belong. This scheme shows high recognition accuracy in the classification of Brodatz texture images. It can also be expanded to texture segmentation using a KL-divergence between two Gaussian mixtures. The proposed method is successfully applied to Brodatz mosaic image segmentation and fabric defect detection.