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  Kernel Methods in Computer Vision

Lampert, C. (2009). Kernel Methods in Computer Vision. In Foundations and Trends in Computer Graphics and Vision (pp. 193-285). Boston, MA, USA: Now Publ.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C2EC-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BE34-E
Genre: Book Chapter

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 Creators:
Lampert, CH1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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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 Dates: 2009
 Publication Status: Published in print
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 Identifiers: DOI: 10.1561/0600000027
BibTex Citekey: 6085
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Title: Foundations and Trends in Computer Graphics and Vision
Source Genre: Series
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Publ. Info: Boston, MA, USA : Now Publ.
Pages: - Volume / Issue: 4,3 Sequence Number: - Start / End Page: 193 - 285 Identifier: ISBN: 978-1-60198-268-1