Fusing cortex transform and intensity based features for image texture classification

This paper proposes a new scheme of fusing cortex transform and brightness based features obtained by local windowing operation. Energy features are obtained by applying popular cortex transform technique within a sliding window rather than the conventional way, while we define three features namely directional surface density (DSD), normalised sharpness index (NSI), and normalized frequency index (NFI) as measures for pixel brightness variation. Fusion by simply vector tagging as well as by correlation is performed in the feature space and then classification is done using minimum distance classifier on the fused vectors. It is interesting that the brightness features, though inferior on some natural images, often produces smoother texture boundary in mosaic images, whereas energy features show the opposite behavior. This symmetrically inverse property is combined through vector fusion for robust classification of multi-texture images obtained from Brodatz album and VisTex database. Classification outcome with confusion matrix analysis shows the robustness of the scheme.