title: Edge-moment-based color constancy using illumination-coherent regularized regression.
abstract: Considering no previous literature reveals the effectiveness of image similarity coherent with corresponding illuminant in color constancy, we propose an edge-moment-based algorithm using regularized regression in an illumination-coherent space in a divide-and-conquer way. To represent the scene images, we adopt color edge moments which are then projected into an illumination-coherent space using canonical correlation analysis (CCA). Further, a mixture of Gaussians (MoG) model is exploited to construct consistent subspaces, in each of which an iterative l2-norm regularized regression is used to learn the correlation between edge moments and illuminants. In the testing phase, estimations from each subspace are fused in a soft way according to the posterior possibility of the test image caused by the MoG. Extensive experiments on the standard datasets including the intra- and inter-dataset evaluations show that our approach outperforms the state-of-the-art algorithms.
authors.forename: Meng
authors.initials: M
authors.lastname: Wu
authors.forename: Kai
authors.initials: K
authors.lastname: Luo
authors.forename: Jianjun
authors.initials: J
authors.lastname: Dang
authors.forename: Jun
authors.initials: J
authors.lastname: Zhou