Abstract
A new algorithm based on wavelet packet transform and singular value decomposition is proposed in this paper for classification of textures in the presence of noise. Lower singular values are affected more by noise than higher singular values, and hence only higher singular values are used to classify textures in the presence of noise. The probability density function of the selected singular values is then modeled as an exponential distribution, and the model parameter for the distribution is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband is used as features for the classification. The classification is carried out using Weighted Probabilistic Neural Networks (WPNN). Compared to conventional probabilistic neural networks, WPNN includes weighting factors between pattern layer and summation layer of the conventional PNN. Performance of the algorithm is compared with `wavelet domain generalized Gaussian density based model' in terms of signal to noise ratio and classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.