None of the existing color constancy algorithms can be considered universal. Furthermore, they use all the image pixels, although actually not all of the pixels are effective in illumination estimation. Consequently, how to select a proper color constancy algorithm from existing algorithms and how to select effective(or useful) pixels from an image are two most important problems for natural images color constancy. In this paper, a novel Color Constancy method using Effective Regions (CCER) is proposed, which consists of the proper algorithm selection and effective regions selection. For a given image, the most proper algorithm is selected according to its Weilbull distribution while its effective regions are chosen based on image similarity. The experiments show promising results compared with the state-of-the-art methods.

Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.