The importance of the depth for text-image selection strategy in learning-to-rank

Authors:
David Buffoni;Sabrina Tollari;Patrick Gallinari
Affiliations:
Université Pierre et Marie Curie, Paris, France;Université Pierre et Marie Curie, Paris, France;Université Pierre et Marie Curie, Paris, France
Venue:
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Year:
2011

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Abstract

We examine the effect of the number documents being pooled, for constructing training sets, has on the performance of the learning-torank (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.