  Computational color constancy is a very important topic in computer vision
and has attracted many researchers' attention. Recently, lots of research has
shown the effects of high level visual content information for illumination
estimation. However, all of these existing methods are essentially
combinational strategies in which image's content analysis is only used to
guide the combination or selection from a variety of individual illumination
estimation methods. In this paper, we propose a novel bilayer sparse coding
model for illumination estimation that considers image similarity in terms of
both low level color distribution and high level image scene content
simultaneously. For the purpose, the image's scene content information is
integrated with its color distribution to obtain optimal illumination
estimation model. The experimental results on two real-world image sets show
that our algorithm is superior to other prevailing illumination estimation
methods, even better than combinational methods.
