The uncertain representation ranking framework for concept-based video retrieval

  • Authors:

  • Robin Aly;Aiden Doherty;Djoerd Hiemstra;Franciska Jong;Alan F. Smeaton

  • Affiliations:

  • Database Group and Human Media Interaction Group, University of Twente, Enschede, The Netherlands;British Heart Foundation Health Promotion Research Group, University of Oxford, Oxford, UK and CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland;Database Group and Human Media Interaction Group, University of Twente, Enschede, The Netherlands;Database Group and Human Media Interaction Group, University of Twente, Enschede, The Netherlands;CLARITY: Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland

  • Venue:
  • Information Retrieval
  • Year:
  • 2013

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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.