HEAT: Iterative relevance feedback with one million images

Authors:
Nicolae Suditu;Francois Fleuret
Affiliations:
Idiap Research Institute and École Polytechnique, Fédérale de Lausanne (EPFL), Switzerland;Idiap Research Institute and École Polytechnique, Fédérale de Lausanne (EPFL), Switzerland
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

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Iterative relevance feedback with adaptive exploration/exploitation trade-off

Proceedings of the 21st ACM international conference on Information and knowledge management

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Abstract

It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images.