PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Canonical Correlation Analysis: An Overview with Application to Learning Methods
David Hardoon, Sandor Szedmak and John Shawe-Taylor
Neural Computation Volume 16, Number 12, pp. 2639-2664, 2004.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1044
Deposited By:David Hardoon
Deposited On:08 August 2005