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Learning Outdoor Color Classification

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1 Author(s)
Manduchi, R. ; Dept. of Comput. Eng., California Univ., Santa Cruz, CA

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

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 11 )

Date of Publication:

Nov. 2006

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