@inproceedings{4e0b432e702445c8b9b9957cdab0d25d,
title = "Probabilistic Approaches to Video Retrieval",
abstract = "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{\textquoteright}s information need.",
keywords = "DB-MMR: MULTIMEDIA RETRIEVAL, METIS-225879, IR-63506, EWI-7265",
author = "Tzvetanka Ianeva and L. Boldareva and T.H.W. Westerveld and Roberto Cornacchia and Djoerd Hiemstra and {de Vries}, A.P.",
note = "Imported from EWI/DB PMS [db-utwente:inpr:0000003689]; null ; Conference date: 15-11-2004 Through 16-11-2004",
year = "2005",
month = mar,
language = "Undefined",
isbn = "not assigned",
series = "TREC Video Retrieval Evaluation Online Proceedings",
publisher = "NIST Special Publications",
pages = "--",
booktitle = "TREC Video Retrieval Evaluation Online Proceedings (TRECVID 2004)",
}