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Learning Outdoor Color Classification
November 2006 (vol. 28 no. 11)
pp. 1713-1723
We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training image. Then, using a simple diagonal illumination model, the illuminants in a new scene that contains some of the surface classes seen in the training image are estimated in a maximum likelihood framework using the Expectation Maximization algorithm. We also show how to impose priors on the illuminants, effectively computing a maximum a posteriori estimation. Experimental results are provided to demonstrate the performance of our classification algorithm in the case of outdoor images.

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Index Terms:
Color constancy, classification, expectation maximization.
Citation:
Roberto Manduchi, "Learning Outdoor Color Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1713-1723, Nov. 2006, doi:10.1109/TPAMI.2006.231
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