Color Cat: Remembering Colors for Illumination Estimation

摘要

Having images look the same regardless of the scene illumination is a desirable feature called color constancy. In this paper the Color Cat (CC), a novel fast and accurate learning-based method for achieving computational color constancy is proposed. It learns and then uses the relationship between transformed color histograms and the regularity in the possible illumination colors. The proposed method is tested on a publicly available color constancy dataset and it is shown to outperform most of the other color constancy methods in terms of accuracy and computation cost. The results are presented and discussed. The source code is available at .