%0 Journal Article
%T Multi-Illuminant Estimation with Conditional Random Fields
%A Shida Beigpour
%A Christian Riess
%A Joost Van de Weijer
%A Elli Angelopoulou
%J IEEE Transactions on Image Processing
%D 2014
%V 23
%N 1
%@ 1057-7149
%F Shida Beigpour2014
%O CIC; LAMP; 600.074; 600.079
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2451), last updated on Fri, 04 Feb 2022 13:11:14 +0100
%X 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 data set of two-dominant-illuminant 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 data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.
%K color constancy
%K CRF
%K multi-illuminant
%U http://refbase.cvc.uab.es/files/BRW2014.pdf
%U http://dx.doi.org/10.1109/TIP.2013.2286327
%P 83-95