The Importance of the Depth for Text-Image Selection Strategy in Learning-To-Rank

  • David Buffoni
  • Sabrina Tollari
  • Patrick Gallinari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)


We examine the effect of the number documents being pooled, for constructing training sets, has on the performance of the learning-to-rank (LTR) approaches that use it to build our ranking functions. Our investigation takes place in a multimedia setting and uses the ImageCLEF photo 2006 dataset based on text and visual features. Experiments show that our LTR algorithm, OWPC,outperforms other baselines.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • David Buffoni
    • 1
  • Sabrina Tollari
    • 1
  • Patrick Gallinari
    • 1
  1. 1.LIP6Université Pierre et Marie Curie - Paris 6ParisFrance

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