Probabilistic Approaches to Video Retrieval

Tzvetanka Ianeva, L. Boldareva, T.H.W. Westerveld, Roberto Cornacchia, Djoerd Hiemstra, A.P. de Vries

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Our experiments for TRECVID 2004 further investigate the applicability of the so-called “Generative Probabilistic Models to video retrieval��?. TRECVID 2003 results demonstrated that mixture models computed from video shot sequences improve the precision of “query by examples��? results when compared to models computed from keyframes. This year, we extended these video models to capture more complex temporal events, by building generative probabilistic models from the shots using the full covariance matrix instead of a diagonal covariance matrix. Also, we improved upon the models of the associated textual data (from ASR and OCR) by introducing a multi-layered hierarchical language model. Finally, we tried to take advantage of the information in the audio channel. In the interactive experiments, we experimented with the automatic selection of the media representation (visual, textual or combined) that is most informative for answering the user’s information need.
Original languageUndefined
Title of host publicationTREC Video Retrieval Evaluation Online Proceedings (TRECVID 2004)
Place of PublicationGaithersburg, MD, USA
PublisherNIST Special Publications
Number of pages10
ISBN (Print)not assigned
Publication statusPublished - Mar 2005
EventTREC Video Retrieval Evaluation, TRECVID 2004 - Gaithersburg, MD, USA
Duration: 15 Nov 200416 Nov 2004

Publication series

NameTREC Video Retrieval Evaluation Online Proceedings
PublisherNational Institute of Standards and Technology (NIST)
VolumeTRECVID 2004


WorkshopTREC Video Retrieval Evaluation, TRECVID 2004
Other15-16 November 2004


  • METIS-225879
  • IR-63506
  • EWI-7265

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