Texture classification using wavelets with a cluster-based feature extraction

After several decades of research, the development of an effective feature extraction method for texture classification is still an ongoing effort. In this paper, we propose a novel approach for texture classification using a new cluster-based feature extraction method that divides the matrices of computed two-dimensional wavelet coefficients into clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors serve as input patterns to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to the performance obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.