An empirical comparison of K-SVD and GMRA for dictionary learning

Vipin Vijayan; Wesam Sakla

doi:10.1117/12.2180022

20 April 2015 An empirical comparison of K-SVD and GMRA for dictionary learning

Vipin Vijayan, Wesam Sakla

Proceedings Volume 9477, Optical Pattern Recognition XXVI; 94770J (2015) https://doi.org/10.1117/12.2180022
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

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Vipin Vijayan and Wesam Sakla "An empirical comparison of K-SVD and GMRA for dictionary learning", Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770J (20 April 2015); https://doi.org/10.1117/12.2180022

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KEYWORDS

Associative arrays

Reconstruction algorithms

Light sources and illumination

Image restoration

Chemical species

Principal component analysis

Compressed sensing

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