## Abstract

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 of 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.

Original language | English (US) |
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Pages (from-to) | 108-119 |

Number of pages | 12 |

Journal | Proceedings of SPIE - The International Society for Optical Engineering |

Volume | 3656 |

State | Published - Jan 1 1999 |

Externally published | Yes |

Event | Proceedings of the 1999 7th Conference of the Storage and Retrieval for Image and Video Databases VII - San Jose, Ca, USA Duration: Jan 26 1999 → Jan 29 1999 |

## ASJC Scopus subject areas

- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering