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Proceedings Paper

Complex function estimation using a stochastic classification/regression framework: specific applications to image superresolution
Author(s): Karl Ni; Truong Q. Nguyen
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Paper Abstract

A stochastic framework combining classification with nonlinear regression is proposed. The performance evaluation is tested in terms of a patch-based image superresolution problem. Assuming a multi-variate Gaussian mixture model for the distribution of all image content, unsupervised probabilistic clustering via expectation maximization allows segmentation of the domain. Subsequently, for the regression component of the algorithm, a modified support vector regression provides per class nonlinear regression while appropriately weighting the relevancy of training points during training. Relevancy is determined by probabilistic values from clustering. Support vector machines, an established convex optimization problem, provide the foundation for additional formulations of learning the kernel matrix via semi-definite programming problems and quadratically constrained quadratic programming problems.

Paper Details

Date Published: 8 October 2007
PDF: 15 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 66960V (8 October 2007); doi: 10.1117/12.740202
Show Author Affiliations
Karl Ni, Univ. of California, San Diego (United States)
Truong Q. Nguyen, Univ. of California, San Diego (United States)

Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)

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