<title>Efficient video sequence retrieval in large repositories</title>

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.