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Kernel Methods in Computer Vision

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84037

Lampert,  CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

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Zitation

Lampert, C. (2009). Kernel Methods in Computer Vision. Foundations and Trends in Computer Graphics and Vision, 4(3), 193-285. doi:10.1561/0600000027.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C2EC-5
Zusammenfassung
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.