  Computational color constancy that requires esti- mation of illuminant colors
of images is a fundamental yet active problem in computer vision, which can be
formulated into a regression problem. To learn a robust regressor for color
constancy, obtaining meaningful imagery features and capturing latent
correlations across output variables play a vital role. In this work, we
introduce a novel deep structured-output regression learning framework to
achieve both goals simultaneously. By borrowing the power of deep convolutional
neural networks (CNN) originally designed for visual recognition, the proposed
framework can automatically discover strong features for white balancing over
different illumination conditions and learn a multi-output regressor beyond
underlying relationships between features and targets to find the complex
interdependence of dif- ferent dimensions of target variables. Experiments on
two public benchmarks demonstrate that our method achieves competitive
performance in comparison with the state-of-the-art approaches.
