A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches

  • Sabrina Tollari
  • Philippe Mulhem
  • Marin Ferecatu
  • Hervé Glotin
  • Marcin Detyniecki
  • Patrick Gallinari
  • Hichem Sahbi
  • Zhong-Qiu Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

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 efficient than non-adapted 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sabrina Tollari
    • 1
  • Philippe Mulhem
    • 2
  • Marin Ferecatu
    • 3
  • Hervé Glotin
    • 4
  • Marcin Detyniecki
    • 1
  • Patrick Gallinari
    • 1
  • Hichem Sahbi
    • 3
  • Zhong-Qiu Zhao
    • 4
    • 5
  1. 1.Université Pierre et Marie Curie - Paris 6, UMR CNRS 7606 LIP6Paris
  2. 2.Université Joseph Fourier, UMR CNRS 5217 LIGGrenoble
  3. 3.TELECOM ParisTech, UMR CNRS 5141 LTCIParis
  4. 4.Université du Sud Toulon-Var, UMR CNRS 6168 LSIS, ToulonFrance
  5. 5.Computer and Information SchoolHefei University of TechnologyChina

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