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
Authors: Vazquez-Corral, Javier|||0000-0003-0414-7096, Vanrell i Martorell, Maria Isabel|||0000-0002-1567-9293, Baldrich i Caselles, Ramon|||0000-0002-0596-7603, Tous Prieto, Francesc|||0000-0002-5722-0341
Format: article
Publication Date:2012
Country:España
Institution:Universitat Autònoma de Barcelona
Repository:Dipòsit Digital de Documents de la UAB
Language:English
OAI Identifier:oai:ddd.uab.cat:287985
Online Access:https://ddd.uab.cat/record/287985
https://dx.doi.org/urn:doi:10.1109/TIP.2011.2171353
Access Level:Open access
Keyword:Category correlation
Color categories
Color constancy
Color naming
Description
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.