DBScholar

Back to papers

Effective Data Co-Reduction for Multimedia Similarity Search

Summary: Data Co-Reduction (DCR) uses co-clustering to jointly compress data and features for similarity search, with lossless distance bounds. Optimal co-reduction minimizes candidates, enabling lossless retrieval and beating prior methods on high-D data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4513
Venue
SIGMOD
Year
2011
Pagerank
5.093636e-05
Overall Rank
12,371 | 15.13%
DOI
10.1145/1989323.1989430

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{huang_sigmod11,
        title = {{Effective Data Co-Reduction for Multimedia Similarity Search}},
        author = {Huang, Zi and Shen, Heng Tao and Liu, Jiajun and Zhou, Xiaofang},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989430},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989430},
        year = {2011}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
4,060 Inter-Media Hashing for Large-scale Retrieval from Heterogeneous Data Sources 2013 SIGMOD 6.9322681e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers