A comparative study of diversity methods for hybrid text and image retrieval approaches

  • Authors:
  • Sabrina Tollari;Philippe Mulhem;Marin Ferecatu;Hervé Glotin;Marcin Detyniecki;Patrick Gallinari;Hichem Sahbi;Zhong-Qiu Zhao

  • Affiliations:
  • Université Pierre et Marie Curie-Paris 6, UMR, CNRS, LIP6, Paris;Université Joseph Fourier, UMR, CNRS, LIG, Grenoble;TELECOM ParisTech, UMR, CNRS, LTCI, Paris;Université du Sud Toulon-Var, UMR, CNRS, LSIS, Toulon;Université Pierre et Marie Curie-Paris 6, UMR, CNRS, LIP6, Paris;Université Pierre et Marie Curie-Paris 6, UMR, CNRS, LIP6, Paris;TELECOM ParisTech, UMR, CNRS, LTCI, Paris;Université du Sud Toulon-Var, UMR, CNRS, LSIS, Toulon and Computer and Information School, Hefei University of Technology, China

  • Venue:
  • CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
  • Year:
  • 2008

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Abstract

This article compares eight different diversity methods: 3 based on visual information, 1 based on date information, 3 adapted to each topic based on location and visual information; finally, for completeness, 1 based on random permutation. To compare the effectiveness of these methods, we apply them on 26 runs obtained with varied methods from different research teams and based on different modalities. We then discuss the results of the more than 200 obtained runs. The results show that query-adapted methods are more effcient than nonadapted method, that visual only runs are more difficult to diversify than text only and text-image runs, and finally that only few methods maximize both the precision and the cluster recall at 20 documents.