Consensus-based framework for illuminant chromaticity estimation

Several algorithms were proposed in the literature to recover the illuminant chromaticity of the original scene. These algorithms work well only when prior assumptions are satisfied, and the best and the worst algorithms may be different for different scenes. We investigate the idea of not relying on a single method but instead consider a consensus decision that takes into account the responses of several algorithms and adaptively chooses the algorithms to be combined. We investigate different combining strategies of state-of-the-art algorithms to improve the results in the illuminant chromaticity estimation. Single algorithms and combined ones are evaluated for both synthetic and real image databases using the angular error between the RGB triplets of the measured illuminant and the estimated one. Being interested in comparing the performance of the methods over large data sets, experimental results are also evaluated using the Wilcoxon signed rank test. Our experiments confirm that the best and the worst algorithms do not exist at all among the state-of-the-art ones and show that simple combining strategies improve the illuminant estimation.