In computer vision fields, LBP histogram selection techniques are mainly applied to reduce the dimension of color texture space in order to increase the classification performances. This paper proposes a new histogram selection score based on Jeffrey distance and sparse similarity matrix obtained by sparse representation. Experimental results on three benchmark texture databases show that the proposed method improves the performance of color texture classification represented in different color spaces.