Multiscale roughness measure for color image segmentation

Color image segmentation is always an important technique in image processing system. Highly precise segmentation with low computation complexity can be achieved through roughness measurement which approximate the color histogram based on rough set theory. However, due to the imprecise description of neighborhood similarity, the existing roughness measure tends to over-focus on the trivial homogeneous regions but is not accurate enough to measure the color homogeneity. This paper aims to construct a multiscale roughness measure through simulating the human vision. We apply the theories of linear scale-space and rough sets to generate the hierarchical roughness of color distribution under multiple scales. This multiscale roughness can tolerate the disturbance of trivial regions and also can provide the multilevel homogeneity representation in vision, which therefore produces precise and intuitive segmentation results. Furthermore, we propose roughness entropy for scale selection. The optimal scale for segmentation is decided by the entropy variation. The proposed method shows the encouraging performance in the experiments based on Berkeley segmentation database.