Long Term Learning for Image Retrieval over Networks

Abstract : In this paper, we present a long term learning system for con- tent based image retrieval over a network. Relevant feedback is used among different sessions to learn both the similarity function and the best routing for the searched category. Our system is based on mobile agents crawling the network in search of relevant images. An ant-behavior algorithm is used to learn the category dependent routing. With experiments on trecvid'05 key-frame dataset, we show that the smart associ- ation of category dependent routing and active learning leads to an improvement of the quality of the retrieval.
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https://hal.archives-ouvertes.fr/hal-00448953
Contributor : Arnaud Revel <>
Submitted on : Wednesday, January 20, 2010 - 3:15:12 PM
Last modification on : Wednesday, October 2, 2019 - 8:14:01 AM

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David Picard, Arnaud Revel, Matthieu Cord. Long Term Learning for Image Retrieval over Networks. ICIP'08 - 15th IEEE International Conference on Image Processing, Oct 2008, San Diego, CA, United States. pp.929-932, ⟨10.1109/ICIP.2008.4711908⟩. ⟨hal-00448953⟩

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