PBIR - Perception-Based Image Retrieval

Summary: PBIR proposes perception-based image retrieval, grounding similarity in human perception and delivering a practical system. It learns the query concept via intelligent sampling, achieving accurate similarity with only a small set of labeled examples. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3381
Venue: SIGMOD
Year: 2001
Pagerank: -
Overall Rank: 14,029 | 3.75%
DOI: 10.1145/375663.375776

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Authors

1. Edward Chang (University of California Santa Barbara)
2. Kwang-Ting (Tim) Cheng (University of California Santa Barbara)
3. Lihyuarn L. Chang (Morpho Software, Inc.)

BibTeX Citation

@inproceedings{chang_sigmod01,
        title = {{PBIR - Perception-Based Image Retrieval}},
        author = {Chang, Edward and Cheng, Kwang-Ting (Tim) and Chang, Lihyuarn L.},
        series = {{SIGMOD} '01},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/375663.375776},
        url = {https://dl.acm.org/doi/10.1145/375663.375776},
        year = {2001}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

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

Rank	Cited Paper	Year	Venue	Pagerank

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