PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Tangent Vector Kernels for Invariant Image Classification with SVMs
Alexei Pozdnoukov and Samy Bengio
In: International Conference on Pattern Recognition, ICPR'2004, August 2004, Cambridge, UK.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:123
Deposited By:Samy Bengio
Deposited On:27 May 2004