Learning outdoor color classification.

Roberto Manduchi

doi:10.1109/tpami.2006.231

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PubMed · 17063678

Learning outdoor color classification.

Roberto Manduchi

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Abstract

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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Algorithms Artificial Intelligence Cluster Analysis Color Colorimetry Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Pattern Recognition, Automated Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Roberto Manduchi. 2006. Learning outdoor color classification.. https://doi.org/10.1109/tpami.2006.231

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