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Proceedings Paper

Efficient video sequence retrieval in large repositories
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Paper Abstract

This paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors, which characterize video data. Indexing high-dimensional vectors is well known to be computationally expensive. Our solution is to optimally split the high dimensional vector into a few low dimensional feature vectors and querying the system for each feature vector. This involves solving an important subproblem: developing a model of retrieval which enables us to query the system efficiently. Once we formulate the retrieval problem in terms of a retrieval model, we present an optimality criterion to maximize the number of results using this model. The criterion is based on a novel idea of using the underlying probability distribution of the feature vectors. A branch-and-prune strategy optimized per each query, is developed. This uses the set of features derived from the optimality criterion. Our results show that the algorithm performs well, giving a speedup of a factor of 25 with respect to a linear search, while retaining the same level of recall.

Paper Details

Date Published: 17 December 1998
PDF: 12 pages
Proc. SPIE 3656, Storage and Retrieval for Image and Video Databases VII, (17 December 1998); doi: 10.1117/12.333831
Show Author Affiliations
Hari Sundaram, Columbia Univ. (United States)
Shih-Fu Chang, Columbia Univ. (United States)

Published in SPIE Proceedings Vol. 3656:
Storage and Retrieval for Image and Video Databases VII
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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