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The uncertain representation ranking framework for concept-based video retrieval
Aly, Robin; Doherty, Aiden R.; Hiemstra, Djoerd; de Jong, Franciska; Smeaton, Alan F.
Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance.
Keyword(s): Machine learning; Multimedia systems; Digital video; Information retrieval; semantic concepts; concept detection; data fusion; Representation uncertainty; Concept-based representation; Video retrieval
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Aly, Robin, Doherty, Aiden R. ORCID: 0000-0003-1840-0451 <>, Hiemstra, Djoerd, de Jong, Franciska and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <> (2012) The uncertain representation ranking framework for concept-based video retrieval. Information Retrieval, 15 . pp. 1-27. ISSN 1386-4564
Publisher(s): Kluwer (Springer)
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2012-07-26 05:05:21 Last Updated: 2019-02-09 06:43:25