A two-level classification-based color constancy

This paper addresses color constancy, the problem of finding true color of objects independent of the light illuminating the scene. However, many algorithms exist in this scope, they are all based on specific assumptions and none of them is universal. Therefore, in order to achieve better performance, some new color constancy methods have been proposed, which most of them are combinational algorithms. In this paper, a new combinational method is proposed; the proposed method consists of two steps: first, a classifier is used to determine the best group of color constancy algorithms for the input image; then, some of the algorithms in this group are combined to estimate the scene illuminant. In this way, it always combines the algorithms that have good performance for the input image, and as a result, the overall performance increases. The proposed method has been evaluated using multiple benchmark datasets, and the experimental results showed that the proposed approach outperformed current state-of-the-art algorithms.