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Abstract:
We formulate the problem of retrieving images from visual
databases as a problem of Bayesian inference. This leads to natural
and effective solutions for two of the most challenging issues in
the design of a retrieval system: providing support for
region-based queries without requiring prior image segmentation,
and accounting for user-feedback during a retrieval session. We
present a new learning algorithm that relies on belief propagation
to account for both positive and negative examples of the user's
interests.
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