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Advances in Information Retrieval

Volume 5478 of the series Lecture Notes in Computer Science pp 126-137

Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval

  • Martin HalveyAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , P. PunithaAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , David HannahAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , Robert VillaAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , Frank HopfgartnerAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , Anuj GoyalAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow
  • , Joemon M. JoseAffiliated withLancaster UniversityDepartment of Computing Science, University of Glasgow

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Abstract

In this paper we present a number of methods for re-ranking video search results in order to introduce diversity into the set of search results. The usefulness of these approaches is evaluated in comparison with similarity based measures, for the TRECVID 2007 collection and tasks [11]. For the MAP of the search results we find that some of our approaches perform as well as similarity based methods. We also find that some of these results can improve the P@N values for some of the lower N values. The most successful of these approaches was then implemented in an interactive search system for the TRECVID 2008 interactive search tasks. The responses from the users indicate that they find the more diverse search results extremely useful.