PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Methods in Computer Vision
Christoph Lampert
Foundations and Trends in Computer Graphics and Vision Volume 4, Number 3, pp. 193-285, 2009.

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6341
Deposited By:Christoph Lampert
Deposited On:08 March 2010