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Canonical Correlation Analysis: An Overview with Application to Learning Methods

Canonical Correlation Analysis: An Overview with Application to Learning Methods
Canonical Correlation Analysis: An Overview with Application to Learning Methods
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 only on their content from a text query. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.
Hardoon, David
e9eb22b2-daf6-460c-94b1-8208c917f862
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Hardoon, David
e9eb22b2-daf6-460c-94b1-8208c917f862
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b

Hardoon, David, Szedmak, Sandor and Shawe-Taylor, John (2004) Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation.

Record type: Article

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 only on their content from a text query. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.

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More information

Published date: 2004
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259778
URI: http://eprints.soton.ac.uk/id/eprint/259778
PURE UUID: fbda72d0-9a3a-43af-a5df-c6f1051686ba

Catalogue record

Date deposited: 02 Mar 2005
Last modified: 16 Jul 2019 22:52

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