2004 | OriginalPaper | Buchkapitel
Learning Outdoor Color Classification from Just One Training Image
verfasst von : Roberto Manduchi
Erschienen in: Computer Vision - ECCV 2004
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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 same surface classes 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 show the excellent performances of our classification algorithm for outdoor images.