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Proceedings Paper

Indexing technique for similarity matching in large video databases
Author(s): Sanghyun Park; June-Suh Cho; Ki-Ho Hyun
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Paper Abstract

Similarity matching in video databases is of growing importance in many new applications such as video clustering and digital video libraries. In order to provide efficient access to relevant data in large databases, there have been many research efforts in video indexing with diverse spatial and temporal features. However, most of the previous works relied on sequential matching methods or memory-based inverted file techniques, thus making them unsuitable for a large volume of video databases. In order to resolve this problem, this paper proposes an effective and scalable indexing technique using a trie, originally proposed for string matching, as an index structure. For building an index, we convert each frame into a symbol sequence using a window order heuristic and build a disk-resident trie from a set of symbol sequences. For query processing, we perform a depth-first search on the trie and execute a temporal segmentation. To verify the superiority of our approach, we perform several experiments with real and synthetic data sets. The results reveal that our approach consistently outperforms the sequential scan method, and the performance gain is maintained even with a large volume of video databases.

Paper Details

Date Published: 19 December 2001
PDF: 9 pages
Proc. SPIE 4676, Storage and Retrieval for Media Databases 2002, (19 December 2001); doi: 10.1117/12.451093
Show Author Affiliations
Sanghyun Park, IBM Thomas J. Watson Research Ctr. (United States)
June-Suh Cho, IBM Thomas J. Watson Research Ctr. (United States)
Ki-Ho Hyun, YoungSan Univ. (South Korea)


Published in SPIE Proceedings Vol. 4676:
Storage and Retrieval for Media Databases 2002
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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