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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Glotin, H., Fakeri-Tabrizi, A., Mulhem, P., Ferecatu, M., Zhao, Z.-Q., Tollari, S., Quenot, G., Sahbi, H., Dumont, E., Gallinari, P.: Comparison of various AVEIR visual concept detectors with an index of carefulness. In: CLEF Working Notes (2009)Google Scholar
  2. 2.
    Joachims, T.: A support vector method for multivariate performance measures. In: International Conference on Machine Learning, ICML (2005)Google Scholar
  3. 3.
    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

Personalised recommendations