A discriminative approach for the retrieval of images from text queries

Abstract. This work proposes a new approach to the retrieval of images from text queries. Con-trasting with previous work, this method relies on a discriminative approach: 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 min-imize 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. 2 IDIAP–RR 06-15