Color Constancy using Natural Image Statistics

Although many color constancy methods exist, they are all based on specific assumptions such as the set of possible light sources, or the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that induces equivalent classes for different image characteristics. Furthermore, the subsequent question is how to combine the different algorithms in a proper way. To achieve selection and combining of color constancy algorithms, in this paper, natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g. texture and contrast) is used. Experiments show that, on a large data set of 11,000 images, our approach outperforms current state-of-the-art single algorithms, as well as simple alternatives for combining several algorithms.