Multi-objective optimization based color constancy

• Analyzing the performance of 75 color constancy algorithms on a benchmark dataset.
• Proposing some new features to classify the image based on them.
• Proposing a multi objective method to combine algorithms in each class.
• Estimating the scene illuminant in Pareto-optimal set based on an anthropological study.

This paper presents a new combining approach for color constancy, the problem of finding the true color of objects independent of the light illuminating the scene. There are various combining methods in the literature that all of them use weighting approach with either pre-determined static weights for all images or dynamically computed weights for each image. The problem with weighting approach is that due to the inherent characteristics of color constancy methods, finding suitable weights for combination is a difficult and error-prone task. In this paper, a new optimization based combining method is proposed which does not need explicit weight assignment. The proposed method has two phases: first, the best group of color constancy algorithms for the given image is determined and then, some of the algorithms in this group are combined using multi-objective optimization methods. To the best of our knowledge, this is the first time that optimization methods are used in color constancy problem. The proposed method has been evaluated using two benchmark datasets and the experimental results were satisfactory in compare with state of the art algorithms.

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