Hari Sundaram, Shih-Fu Chang. Efficient Video Sequence Retrieval in Large Repositories. In IS&T/SPIE Symposium on Electronic Imaging: Science and Technology (EI'99) - Visual Communications and Image Processing (VCIP), San Jose, CA, January 1999.
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This paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors that 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 sub-problem: developing a model for retrieval that 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
@InProceedings{dvmmPub89,
Author = {Sundaram, Hari and Chang, Shih-Fu},
Title = {Efficient Video Sequence Retrieval in Large Repositories},
BookTitle = {IS&T/SPIE Symposium on Electronic Imaging: Science and Technology (EI'99) - Visual Communications and Image Processing (VCIP)},
Address = {San Jose, CA},
Month = {January},
Year = {1999}
}
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