Texture-based color constancy using local regression

Color constancy endows the machines with the ability of identifying the color regardless of the illuminant. Considering none of single algorithms available is universal, this paper presents a novel combination approach based on local texture features and local regression. To better represent images, local texture features based on integrated Weibull distribution are firstly extracted on the overlapping patches of the images. Then we define a new image distance metric to search for K most similar images of the test image. Incorporating a priori knowledge into the data-driven strategy, we finally combine individual algorithms using a local penalized regression according to the frequency ratio of best single algorithms. Experiment on a widely used dataset shows that the proposed approach outperforms the state-of-the-art single algorithms as well as popular combination approaches.