ML parameter estimation and minimum distance classifier for texture analysis using wavelet transform

This work concentrates on comparing the performance of the minimum distance classifier and maximum-likelihood classifier for texture analysis. A tree-structured wavelet transform has been used for extracting the features and the comparison is based on the correct classification percentage. The results indicate that the maximum-likelihood classifier performs marginally better than the mahalanobis distance for some feature sets. The Euclidean distance did not prove to be powerful in distinguishing the textures. The performance of various orthogonal wavelet transforms have also been compared in order to find out the best wavelet for each of the classifiers considered.