Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

December 1, 2004, Vol. 16, No. 12, Pages 2639-2664
(doi: 10.1162/0899766042321814)
© 2004 Massachusetts Institute of Technology
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Article PDF (714.88 KB)
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.