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 named P₁P₂, 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 P₁P₂ family. The overall classification performance is very promising and the proposed methodology surmounts the current color texture characterization by features extracted from the luminance spectrum only