The uncertain representation ranking framework for concept-based video retrieval

Robin Aly, Aiden Doherty, Djoerd Hiemstra, Franciska M.G. de Jong, Alan F. Smeaton

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
57 Downloads (Pure)

Abstract

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.
Original languageUndefined
Pages (from-to)557-583
Number of pages27
JournalInformation retrieval
Volume16
Issue number5
DOIs
Publication statusPublished - Oct 2013

Keywords

  • Representation Uncertainty · Concept-based Representation · Video Retrieval
  • IR-80911
  • METIS-287734
  • EWI-22070

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