Selection of color texture features from Reduced Size Chromatic Co-occurrence Matrices

In this paper, we present a feature selection scheme which builds a low-dimensional feature space for texture classification. These features are extracted from texture descriptors called Reduced-Size Chromatic Co-occurrence Matrices (RSCCMs) which result from color quantization. Thanks to experimental results achieved with VisTex and OuTex databases, we show that the analysis of Haralick features extracted from these RSCCMs, themselves computed from color images coded in 28 different color spaces, provides satisfying classification results while significantly reducing the processing time.