title: Canonical correlation analysis: an overview with application to learning methods.
abstract: We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.
authors.affiliation: School of Electronics and Computer Science, Image, Speech and Intelligent Systems Research Group, University of Southampton, Southampton S017 1BJ, UK. drh@ecs.soton.ac.uk
authors.forename: David R
authors.initials: DR
authors.lastname: Hardoon
authors.forename: Sandor
authors.initials: S
authors.lastname: Szedmak
authors.forename: John
authors.initials: J
authors.lastname: Shawe-Taylor