CONF pozd:2004:icpr/IDIAP Tangent Vector Kernels for Invariant Image Classification with SVMs Pozdnoukhov, Alexei Bengio, Samy EXTERNAL https://publications.idiap.ch/attachments/reports/2004/pozd-ICPR04.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/rr03-75 Related documents 17th Int. Conf. Pattern Recognition (ICPR) 2004 Cambridge, UK August 2004 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. REPORT rr03-75/IDIAP Tangent Vector Kernels for Invariant Image Classification with SVMs Pozdnoukhov, Alexei Bengio, Samy EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-75.pdf PUBLIC Idiap-RR-75-2003 2003 IDIAP Martigny, Switzerland Submitted to International Conference on Pattern Recognition 2004 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.