Issue No. 11 - November (2006 vol. 28)
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.
Color constancy, classification, expectation maximization.
R. Manduchi, "Learning Outdoor Color Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1713-1723, 2006.