Multi-Illuminant Estimation with Conditional Random Fields

@Article\{BRV2014,
  author       = "Shida Beigpour and Christian Riess and Joost van de Weijer and Elli Angelopoulou",
  title        = "Multi-Illuminant Estimation with Conditional Random Fields",
  journal      = "IEEE Transactions on Image Processing",
  number       = "1",
  volume       = "23",
  pages        = "83--95",
  month        = "jan",
  year         = "2014",
  abstract     = "Most existing color constancy algorithms assume
uniform illumination. However, in real-world scenes, this is
not often the case. Thus, we propose a novel framework for
estimating the colors of multiple illuminants and their spatial
distribution in the scene. We formulate this problem as an
energy minimization task within a Conditional Random Field
over a set of local illuminant estimates. In order to quantitatively
evaluate the proposed method, we created a novel dataset of twodominant-
illuminants images comprised of laboratory, indoor
and outdoor scenes. Unlike prior work, our database includes
accurate pixel-wise ground truth illuminant information. The
performance of our method is evaluated on multiple datasets.
Experimental results show that our framework clearly outperforms
single illuminant estimators, as well as a recently proposed
multi-illuminant estimation approach.",
  url          = "http://cat.cvc.uab.es/Public/Publications/2014/BRV2014"
}