Image classification using the surface-shape operator and multiscale features

This paper introduces new features for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the topographic structure described by the surface-shape operator. Then, illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the multiresolution simultaneous autoregressive features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures (1966).