The effect multiple query representations on information retrieval system performance
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SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may utilize the information contained in multiple queries (gotten in one step or through a feedback process), this is often only a reformulation of the original query. As a result most of these strategies only get the images in some neighborhood of the original query as the retrieval result. This severely restricts the system performance. Relevance feedback techniques are generally used to mitigate this problem. In this paper, we present a novel approach to relevance feedback which can return semantically related images in different visual clusters by merging the result sets of multiple queries. We also provide experimental results to demonstrate the effectiveness of our approach.