This paper presents and compares a new adapted version of the Laplacian score used to select LBP histogram for color texture classification. During a supervised learning stage, we first compute a similarity matrix between images using the true class labels of these images. Then, a score is attributed to each histogram. This score allows to measure the capability of the histogram of preserving the similarity matrix. The histograms are then ranked according to the proposed score and the most discriminant ones are selected. Experiments are achieved on benchmark color texture image databases in order to show the interest of the proposed score for histogram selection.