A Discriminative Approach for the Retrieval of Images from Text Queries
David Grangier, Florent Monay and Samy Bengio
In: European Conference on Machine Learning (ECML), Sept 2006, Berlin, Germany.
This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the model, i.e. its ability to rank the relevant pictures above the non-relevant ones when given a text query. In order to minimize this loss, we introduce an adaptation of the recently proposed Passive-Aggressive algorithm. The generalization performance of this approach is then compared with alternative models over the Corel dataset. These experiments show that our method outperforms the current state-of-the-art approaches, e.g. the average precision over Corel test data is 21.6\% for our model versus 16.7\% for the best alternative, Probabilistic Latent Semantic Analysis.