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Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval

Summary: Qcluster introduces adaptive classification and cluster-merging for disjunctive, multi-cluster relevance feedback in large-scale image retrieval. It uses linear-transform invariant measures to make retrieval robust to cluster shapes, achieving significant gains over query expansion and query-point movement on MARS with fast convergence to the user’s true need. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3528
Venue
SIGMOD
Year
2003
Pagerank
5.3088343e-05
Overall Rank
9,174 | 37.06%
DOI
10.1145/872757.872829

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kim_sigmod03,
        title = {{Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval}},
        author = {Kim, Deok-Hwan and Chung, Chin-Wan},
        series = {{SIGMOD} '03},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/872757.872829},
        url = {https://dl.acm.org/doi/10.1145/872757.872829},
        year = {2003}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
5,966 VOCAL: Video Organization and Interactive Compositional AnaLytics 2022 CIDR 6.0255527e-05
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Outgoing Citations (Sorted by Pagerank)

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