Using Visual Concepts and Fast Visual Diversity to Improve Image Retrieval

  • Sabrina Tollari
  • Marcin Detyniecki
  • Ali Fakeri-Tabrizi
  • Christophe Marsala
  • Massih-Reza Amini
  • Patrick Gallinari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

In this article, we focus our efforts (i) on the study of how to automatically extract and exploit visual concepts and (ii) on fast visual diversity. First, in the Visual Concept Detection Task (VCDT), we look at the mutual exclusion and implication relations between VCDT concepts in order to improve the automatic image annotation by Forest of Fuzzy Decision Trees (FFDTs). Second, in the ImageCLEFphoto task, we use the FFDTs learnt in VCDT task and WordNet to improve image retrieval. Third, we apply a fast visual diversity method based on space clustering to improve the cluster recall score. This study shows that there is a clear improvement, in terms of precision or cluster recall at 20, when using the visual concepts explicitly appearing in the query and that space clustering can be efficiently used to improve cluster recall.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sabrina Tollari
    • 1
  • Marcin Detyniecki
    • 1
  • Ali Fakeri-Tabrizi
    • 1
  • Christophe Marsala
    • 1
  • Massih-Reza Amini
    • 1
  • Patrick Gallinari
    • 1
  1. 1.Laboratoire d’Informatique de Paris 6 - UMR CNRS 7606Université Pierre et Marie Curie - Paris 6ParisFrance

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