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Kernel Methods in Computer Vision
Foundations and Trends® in
Computer Graphics and Vision
Volume 4 Issue 3
DOI: 10.1561/0600000027
Kernel Methods in Computer Vision
Christoph H. Lampert
Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany,
http://www.christoph-lampert.de, chl@tuebingen.mpg.de
SUGGESTED CITATION:
Christoph H.
Lampert
(2009)
"Kernel Methods in Computer Vision",
Foundations and Trends® in Computer Graphics and Vision: Vol. 4: No 3, pp 193-285.
http://dx.doi.org/10.1561/0600000027
Abstract
Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial,
we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous
support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection, and clustering.
Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation
of statistical dependency, and for learning the kernel function itself. All methods are illustrated with examples of successful
application from the recent computer vision research literature.
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