Learning from User Feedback in Image Retrieval Systems

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

Bibtex Metadata Paper


Nuno Vasconcelos, Andrew Lippman


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.