Efficient video sequence retrieval in large repositories

Hari Sundaram, Shih Fu Chang

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish (US)
Pages (from-to)108-119
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3656
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 7th Conference of the Storage and Retrieval for Image and Video Databases VII - San Jose, Ca, USA
Duration: Jan 26 1999Jan 29 1999

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Efficient video sequence retrieval in large repositories'. Together they form a unique fingerprint.

Cite this