Wavelet-based modeling of singular values for image texture classification

Authors:
S. Ramakrishnan;S. Selvan
Affiliations:
Department of Information Technology, PSG College of Technology, Coimbatore, India;Department of Information Technology, PSG College of Technology, Coimbatore, India
Venue:
Machine Graphics & Vision International Journal
Year:
2006

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Abstract

A new algorithm based on the wavelet packet transform is proposed for the classification of image textures. Energy matrices are formed from subband coefficients of the wavelet packet transform. Singular value decomposition is then employed on the energy matrices. The probability density function of singular values is modeled as exponential distribution, and the model parameter is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband, is used to form the feature vector. Classification is carried out using the Kullback-Leibler Distance (KLD). Performance of the algorithm is compared with model-based and feature-based methods in terms of the signal-to-noise ratio and the classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.