Color Constancy by Category Correlation

Finding color representations that are stable to illuminant changes is still an open problem in computer vision. Until now, most approaches have been based on physical constraints or statistical assumptions derived from the scene, whereas very little attention has been paid to the effects that selec...

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Bibliographic Details
Published in:IEEE transactions on image processing 2012-04, Vol.21 (4), p.1997-2007
Main Authors: Vazquez-Corral, J., Vanrell, M., Baldrich, R., Tous, F.
Format: Article
Language:English
Subjects:
Algorithms
Category correlation
Color
color categories
color constancy
color naming
Colorimetry - methods
Computer vision
Correlation
Equations
Humans
Image color analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Lighting - methods
Probabilistic logic
Reproducibility of Results
Sensitivity and Specificity
Statistical analysis
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Summary:Finding color representations that are stable to illuminant changes is still an open problem in computer vision. Until now, most approaches have been based on physical constraints or statistical assumptions derived from the scene, whereas very little attention has been paid to the effects that selected illuminants have on the final color image representation. The novelty of this paper is to propose perceptual constraints that are computed on the corrected images. We define the category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here, we choose these colors as the universal color categories related to basic linguistic terms, which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis, we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2171353