A Novel Colour-Constancy Algorithm: A Mixture of Existing Algorithms

Colour constancy algorithms attempt to provide an accurate colour representation of images independent of the illuminant colour used for scene illumination. In this paper we investigate well-known and state-ofthe-art colour-constancy algorithms. We then select a few of these algorithms and combine them using a weighted-sum approach. Four methods are involved in the weights estimation. The first method uniformly distributes the weights among the algorithms. The second one uses learning set of images to train the weights based on errors. The third method searches for a linear combination of all methods’ outcomes that minimise the error. The fourth one trains a continuous perceptron, in order to find optimum combination of the methods. In all four approaches, we used a set of 60 images. Each of these images was taken with a Gretag Macbeth colour checker card in the scene, in order to make quantitative evaluation of colour-consistency algorithms. The results obtained show our proposed method outperforms individual algorithms. The best results were obtained using the weights for linear combination and the trained continuous perceptron to combine the algorithms.