To quantify the concept of similarity between classes of images three measures and algorithms of calculation are proposed. The first measure is calculated through the frequency of misclassification of subimages sampled randomly from images. The second one is calculated through the cross membership of the mass center of a class in a feature space. The third measure is defined through the membership of subimages, using the distance between each subimage and the mass center of a class in a feature space. We study these measures, classifying images in the coordinated clusters representation (CCR) feature space with the minimum distance classifier. A database of images of Rosa Porriño granite tiles, previously classified by three human experts, is used in the experiments. The calculated similarity between classes is in excellent accordance with the qualitative evaluation by the human experts.