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Improving Image Annotation in Imbalanced Classification Problems with Ranking SVM

  • Ali Fakeri-Tabrizi
  • Sabrina Tollari
  • Nicolas Usunier
  • Patrick Gallinari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6242)

Abstract

We try to overcome the imbalanced data set problem in image annotation by choosing a convenient loss function for learning the classifier. Instead of training a standard SVM, we use a Ranking SVM in which the chosen loss function is helpful in the case of imbalanced data. We compare the Ranking SVM to a classical SVM with different visual features. We observe that Ranking SVM always improves the prediction quality, and can perform up to 23% better than the classical SVM.

Keywords

Visual Feature Image Annotation Equal Error Rate Ranking Algorithm Imbalanced Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Nowak, S., Dunker, P.: Overview of the CLEF 2009 large scale — visual concept detection and annotation task. In: Peters, C., et al. (eds.) CLEF 2009 Workshop, Part II. LNCS, vol. 6242, pp. 94–109. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ali Fakeri-Tabrizi
    • 1
  • Sabrina Tollari
    • 1
  • Nicolas Usunier
    • 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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