A new approach to feature extraction for wavelet-based texture classification

A new class of features for wavelet-based texture classification is introduced using a new feature-weighting scheme adapted to non-Euclidean similarity measures. The feature extraction is based on the histogram of the local second moment estimates of the wavelet transform. It is shown that the bins´ centers of such histograms should be scaled logarithmically rather than linearly. The distance between two texture features is measured using the x² similarity measure, weighted according to the feature´s degree of dispersion within the training dataset. Classification experiments of the proposed approach using an orthonormal wavelet transform show improved classification results compared to presently available methods.