DBScholar

Back to papers

Challenges and Techniques for Effective and Efficient Similarity Search in Large Video Databases

Summary: Novel scalable methods for content-based video similarity search in large databases. Two tasks: retrieval over segmented clip collections and subsequence identification in unsegmented streams; demonstrated in the UQLIPS prototype with commercialization potential. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9998
Venue
VLDB
Year
2008
Pagerank
5.3015807e-05
Overall Rank
9,238 | 36.62%
DOI
10.14778/1454159.1454230

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{shao_vldb08,
        title = {{Challenges and Techniques for Effective and Efficient Similarity Search in Large Video Databases}},
        author = {Shao, Jie},
        journal = {PVLDB},
        series = {{VLDB} '08},
        volume = {1},
        number = {2},
        pages = {1598--1601},
        doi = {10.14778/1454159.1454230},
        url = {https://doi.org/10.14778/1454159.1454230},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,589 Optimizing Video Selection LIMIT Queries With Commonsense Knowledge 2024 VLDB 5.4065627e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 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