Machine Learning
Iterative refinement by relevance feedback in content-based digital image retrieval
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
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Building a Latent Semantic Index of an Image Database from Patterns of Relevance Feedback
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WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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In this article, we introduce an application of swarm intelligence to distributed visual information retrieval distributed over networks. Based on the relevance feedback scheme, we use ant-like agents to crawl the network and to retrieve relevant images. Agents movements are influenced by markers stored on the hosts. These markers are reinforced to match the distribution of relevant images over the network. We tackle the use of the information gathered during previous search sessions. In order to match the different categories available on the network, we use several markers. Sessions searching for the same category will thus use the same makers. The system involves three learning problems: the selection of relevant markers regarding the searched category, the reinforcement of these markers and the learning of the relevance function. All of these problems are based on the relevance feedback loop. We test our system on a custom network hosting images taken from the well known TrecVid dataset. Our system shows a high improvement over classical content based image retrieval systems which do not use previous sessions information.