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Neural Comput. 2004 Dec;16(12):2639-64.

Canonical correlation analysis: an overview with application to learning methods.

Author information

1
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

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.

PMID:
15516276
DOI:
10.1162/0899766042321814
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