PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using High-Level Visual Information for Color Constancy
Joost van de Weijer, Cordelia Schmid and Jakob Verbeek
In: ICCV(2007).

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 modeled 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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:4216
Deposited By:Jakob Verbeek
Deposited On:03 December 2008