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Towards Effective Indexing for Very Large Video Sequence Database

Summary: Proposes ViTri, a compact cluster model for video frames as hyperspheres (center, radius, density) to estimate sequence similarity by hypersphere overlap. A PCA-based 1D transform enables a B+-tree index on ViTri positions, dramatically reducing similarity computations on large video databases. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3742
Venue
SIGMOD
Year
2005
Pagerank
6.6036373e-05
Overall Rank
4,618 | 68.32%
DOI
10.1145/1066157.1066240

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{shen_sigmod05,
        title = {{Towards Effective Indexing for Very Large Video Sequence Database}},
        author = {Shen, Heng Tao and Ooi, Beng Chin and Zhou, Xiaofang},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066240},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066240},
        year = {2005}
}

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