Graph-Based Reflectance Segmentation

Most of the unsupervised image segmentation algorithms use just RGB color information in order to establish the similarity criteria between pixels in the image. This leads in many cases to a wrong interpretation of the scene since these criteria do not consider the physical interactions which give raise to of those RGB values (illumination, geometry, albedo) nor our perception of the scene. In this paper, we propose a novel criterion for unsupervised image segmentation which not only relies on color features, but also takes into account an approximation of the materials reflectance. By using a perceptually uniform color space, we apply our criterion to one of the most relevant state of the art segmentation techniques, showing its suitability for segmenting images into small and coherent clusters of constant reflectance. Furthermore, due to the wide adoption of such algorithm, we provide for the first time in the literature an evaluation of this technique under several scenarios and different configurations of its parameters. Finally, in order to enhance both the accuracy of the segmentation and the inner coherence of the clusters, we apply a series of image processing filters to the input image (median, mean-shift, bilateral), analyzing their effects in the segmentation process. Our results can be transferred to any image segmentation algorithm.