Efficient and Cost-effective Techniques for Browsing and Indexing Large Video Databases
Summary: Content-based, fully automatic video indexing from shot segmentation with camera-tracking; uses Var^BA and Var^OA to encode background/foreground change. Builds a browsing hierarchy and a variance-based index for efficient similarity search. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. JungHwan Oh (University of Central Florida)
- 2. Kien A. Hua (University of Central Florida)
BibTeX Citation
@inproceedings{oh_sigmod00,
title = {{Efficient and Cost-effective Techniques for Browsing and Indexing Large Video Databases}},
author = {Oh, JungHwan and Hua, Kien A.},
series = {{SIGMOD} '00},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/342009.335436},
url = {https://dl.acm.org/doi/10.1145/342009.335436},
year = {2000}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 284 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00022370521 |
| 569 | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics | 2020 | VLDB | 0.00016348191 |
| 5,827 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 6.0744172e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
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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