Color space transformations for analysis and enhancement of ancient degraded manuscripts
Pattern Recognition and Image Analysis
Engineering Applications of Artificial Intelligence
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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 P1P2), which 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 adaptation 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 energetic features extracted from the luminance spectrum only.