Color constancy via convex kernel optimization

Authors:
Xiaotong Yuan;Stan Z. Li;Ran He
Affiliations:
Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Science, Beijing, China;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Science, Beijing, China;Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Science, Beijing, China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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Abstract

This paper introduces a novel convex kernel based method for color constancy computation with explicit illuminant parameter estimation. A simple linear render model is adopted and the illuminants in a new scene that contains some of the color surfaces seen in the training image are sequentially estimated in a global optimization framework. The proposed method is fully data-driven and initialization invariant. Nonlinear color constancy can also be approximately solved in this kernel optimization framework with piecewise linear assumption. Extensive experiments on real-scene images validate the practical performance of our method.