Learning to retrieve images from text queries with a discriminative model

Authors:
David Grangier;Florent Monay;Samy Bengio
Affiliations:
IDIAP Research Institute, Martigny, Switzerland and Ecole Polytechnique Fédérale de Lausanne, Switzerland;IDIAP Research Institute, Martigny, Switzerland and Ecole Polytechnique Fédérale de Lausanne, Switzerland;IDIAP Research Institute, Martigny, Switzerland
Venue:
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Year:
2006

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Abstract

This work presents a discriminative model for the retrieval of pictures from text queries. The core idea of this approach is to minimize a loss directly related to the retrieval performance of the model. For that purpose, we rely on a ranking loss which has recently been successfully applied to text retrieval problems. The experiments performed over the Corel dataset show that our approach compares favorably with generative models that constitute the state-of-the-art (e.g. our model reaches 21.6% mean average precision with Blob and SIFT features, compared to 16.7% for PLSA, the best alternative).