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 [BibTeX] [Marc21]
Tangent Vector Kernels for Invariant Image Classification with SVMs
Type of publication: Idiap-RR
Citation: rr03-75
Number: Idiap-RR-75-2003
Year: 2003
Institution: IDIAP
Address: Martigny, Switzerland
Note: Submitted to International Conference on Pattern Recognition 2004
Abstract: This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invariances. Real data of face images are used for experiments. The presented approach integrates virtual sample and tangent distance methods. We observe a significant increase in performance with respect to standard approaches. The experiments also illustrate (as expected) that prior knowledge becomes more important as the amount of training data decreases.
Userfields: ipdmembership={learning}, language={English},
Keywords:
Projects Idiap
Authors Pozdnoukhov, Alexei
Bengio, Samy
Crossref by pozd:2004:icpr
Added by: [UNK]
Total mark: 0
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  • rr03-75.pdf
  • rr03-75.ps.gz
Notes