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Learning from User Feedback in Image Retrieval Systems
1999
Neural Information Processing Systems
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.
dblp:conf/nips/VasconcelosL99
fatcat:yfqtrmbgszgetiyxazwfhlgaiy