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2004
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Tangent Vector Kernels for Invariant Image Classification with SVMs

9 years 11 months ago
Tangent Vector Kernels for Invariant Image Classification with SVMs
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.
Alexei Pozdnoukhov, Samy Bengio
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Alexei Pozdnoukhov, Samy Bengio
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