Wavelet transform-based texture classification with feature weighting

A new approach to wavelet transform-based texture classification using feature weighting is presented. The Daubechies orthogonal wavelets are selected and the standard pyramid-structured wavelet transform is employed. This approach extracts the l₁-norm, for each frequency channel, of the wavelet transform output as the features for texture classification, and weights these features according to their own degree of dispersion. A minimum distance classifier is used in the classification procedure. The classification experiments for almost 1000 testing samples obtained from 20 classes of textures show that the performance of the presented texture classification algorithm is much better than the algorithm without feature weighting