This paper presents a compact color texture representation based on the selection of features extracted from different configurations of descriptors computed in multiple color spaces. The proposed representation aims to take simultaneously into account several spatial and color properties of different textures. For this purpose, texture images are coded in five different color spaces. Then, texture descriptors with different neighborhood and quantization parameter settings, are calculated from this images in order to extract a high dimensionality feature vector describing the textures. Compact representation is finally obtained by means of a feature selection scheme. Our approach is applied with two well-known color texture descriptors for the classification of three benchmark image databases.