Color Texture Classification by Normalized Color Space Representation

This paper proposes a novel approach to color texture characterization and classification. Rather than developing new textural features, we propose to derive a family of new, reduced dimensionality color spaces (that we call ), that allow a good classification performance by the use of classical energy-distribution features, defined in a scalar spectral domain. The dimensionality reduction approach can be traced back to color constancy normalization and the reduced ordering principle and exhibits a strong perceptual background. We develop an adaption procedure for the selection of the proper color space within the new family. The overall classification performance is very promising and the proposed methodology surmounts the current color texture characterization by energetic features extracted from the luminance spectrum only.