複数テクスチャ分類のための皮質変換と輝度特徴の総合

抄録

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 mesures 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. The interesting point is that 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 properties are combined through vector fusion for robust classification of muti-texture images obtained from Brodatz album and VisTex database. Results and comparison in terms of edge smoothness and confusion matrix based accuracy metrics show the robustness of the scheme.