A Discriminative Approach for the Retrieval of Images from Text Queries

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

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: David Grangier, Florent Monay, Samy Bengio
• Keywords: Current State-of-the-art Approaches, Discriminative Model, ECML 2006, Machine Learning, Text Queries