A probabilistic framework for semantic indexing and retrieval in video

M. R. Naphade, Thomas S Huang

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper proposes a probabilistic framework for semantic indexing and retrieval in digital video. The components of the framework are multijects and multinets. Multijects are probabilistic multimedia objects representing semantic features or concepts. A multinet is a probabilistic network of multijects which accounts for the interaction between concepts. The main contribution of this paper is a Bayesian multinet which enhances the detection probability of individual multijects, provide a unified framework for integrating multiple modalities and supports inference of unobservable concepts based on their relation with observable concepts. We develop multijects for detecting sites (locations) in video and integrate the multijects using a multinet in the form of a Bayesian network. Experiments reveal significant performance improvement using the multinet.

Original languageEnglish (US)
Pages475-478
Number of pages4
StatePublished - Dec 1 2000
Event2000 IEEE Internatinal Conference on Multimedia and Expo (ICME 2000) - New York, NY, United States
Duration: Jul 30 2000Aug 2 2000

Other

Other2000 IEEE Internatinal Conference on Multimedia and Expo (ICME 2000)
CountryUnited States
CityNew York, NY
Period7/30/008/2/00

ASJC Scopus subject areas

  • Engineering(all)

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