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Kernel Methods in Computer Vision




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.

Author(s): Lampert, CH.
Journal: Foundations and Trends in Computer Graphics and Vision
Volume: 4
Number (issue): 3
Pages: 193-285
Year: 2009
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1561/0600000027
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Kernel Methods in Computer Vision},
  author = {Lampert, CH.},
  journal = {Foundations and Trends in Computer Graphics and Vision},
  volume = {4},
  number = {3},
  pages = {193-285},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2009},
  month_numeric = {9}