Learning Outdoor Color Classification

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