
Proceedings Paper
An empirical comparison of K-SVD and GMRA for dictionary learningFormat | Member Price | Non-Member Price |
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Paper Abstract
The topic of constructing data-dependent dictionaries, referred to as dictionary learning, has received considerable
interest in the past decade. In this work, we compare the ability of two dictionary learning algorithms,
K-SVD and geometric multi-resolution analysis (GMRA), to perform image reconstruction using a fixed number
of coefficients. K-SVD is an algorithm originating from the compressive sensing community and relies on
optimization techniques. GMRA is a multi-scale technique that is based on manifold approximation of highdimensional
point clouds of data. The empirical results of this work using a synthetic dataset of images of
vehicles with diversity in viewpoint and lighting show that the K-SVD algorithm exhibits better generalization
reconstruction performance with respect to test images containing lighting diversity that were not present in the
construction of the dictionary, while GMRA exhibits superior reconstruction on the training data.
Paper Details
Date Published: 20 April 2015
PDF: 9 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770J (20 April 2015); doi: 10.1117/12.2180022
Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)
PDF: 9 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770J (20 April 2015); doi: 10.1117/12.2180022
Show Author Affiliations
Vipin Vijayan, Univ. of Notre Dame (United States)
Wesam Sakla, Air Force Research Lab. (United States)
Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)
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