Blending Intensity-contrast and Transform Features for Image Texture Classification

M.K. Bashar, Y. Takeuchi, T. Matsumoto, and N. Ohnishi (Japan)

Keywords

: Cortex transform, texture, mean energy, directional surface density, integration, confusion matrix.

Abstract

We propose a scheme of integrating two feature groups computed with and without transformation of the original images. Transform features are obtained by popular cortex transform technique, while contrast features are computed from the original intensity images. we pro pose three contrast features, namely, directional surface density (DSD), normalised sharpness index (NSI), and normalized frequency index (NFI) as measures for pixel brightness variations. Fusion by simply stacking vectors as well as by correlation is performed in the feature space and then classification is done using minimum distance classifier on the fused vectors. Transform features extract smoother texture boundaries in micro-textured images (natural scenes) while contrast features better extract boundaries in highly (regular) textured images(mosaic images). This inverse properties are combined through vector fusion for robust texture classification of two image groups namely mosaic and natural scenes obtained from Brodatz album and VisTex database respectively. Error matrix and edge-smoothness analysis show the ro bustness of the proposed scheme.

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