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Using High-Level Visual Information for Color Constancy

Joost van de Weijer 1 Cordelia Schmid 1 Jakob Verbeek 1 
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We propose to use high-level visual information to improve illuminant estimation. Several illuminant estimation approaches are applied to compute a set of possible illuminants. For each of them an illuminant color corrected image is evaluated on the likelihood of its semantic content: is the grass green, the road grey, and the sky blue, in correspondence with our prior knowledge of the world. The illuminant resulting in the most likely semantic composition of the image is selected as the illuminant color. To evaluate the likelihood of the semantic content, we apply probabilistic latent semantic analysis. The image is modelled as a mixture of semantic classes, such as sky, grass, road, and building. The class description is based on texture, position and color information. Experiments show that the use of high-level information improves illuminant estimation over a purely bottom-up approach. Furthermore, the proposed method is shown to significantly improve semantic class recognition performance.
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Submitted on : Friday, March 18, 2011 - 2:25:08 PM
Last modification on : Thursday, January 20, 2022 - 5:28:02 PM
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Joost van de Weijer, Cordelia Schmid, Jakob Verbeek. Using High-Level Visual Information for Color Constancy. ICCV 2007 - IEEE 11th International Conference on Computer Vision, Oct 2007, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/ICCV.2007.4409109⟩. ⟨inria-00321125v2⟩



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