Source:http://linkedlifedata.com/resource/pubmed/id/15516276
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
12
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| pubmed:dateCreated |
2004-11-1
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| pubmed:abstractText |
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.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:month |
Dec
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| pubmed:issn |
0899-7667
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
16
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
2639-64
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| pubmed:year |
2004
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| pubmed:articleTitle |
Canonical correlation analysis: an overview with application to learning methods.
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| pubmed: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
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| pubmed:publicationType |
Journal Article
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