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Issue No.04 - April (2011 vol.33)
pp: 687-698
Arjan Gijsenij , University of Amsterdam, Amsterdam
Theo Gevers , Faculty of Science, University of Amsterdam, Amsterdam
ABSTRACT
Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.
INDEX TERMS
Color constancy, illuminant estimation, natural image statistics, scene semantics, computer vision.
CITATION
Arjan Gijsenij, Theo Gevers, "Color Constancy Using Natural Image Statistics and Scene Semantics", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 4, pp. 687-698, April 2011, doi:10.1109/TPAMI.2010.93
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