Image texture classification using wavelet based curve fitting and probabilistic neural network

This article describes a new approach for image texture classification based on curve fitting of wavelet domain singular values and probabilistic neural networks. Image textures are wavelet packet transformed and singular value decomposition is then employed on subband coefficient matrices after introducing non-linearity. Lower singular values are truncated based on energy distribution to effectively classify textures in the presence of noise. The selected singular values are fitted to the exponential curve. The model parameters are estimated using population-sample analogues method and the parameters are used for performing classification. A modified form of probabilistic neural network (PNN) called weighted PNN (WPNN) is employed for performing the classification. Compared to probabilistic neural network, WPNN includes weighting factors between pattern layer and summation layer of the PNN. Performance of the approach is compared with model based and feature based methods in terms of signal to noise ratio and classification rate. Experimental results prove that the proposed approach gives better classification rate under noisy environment. (c) 2007 Wiley Periodicals, Inc.