Sparse Coding Neural Gas Applied to Image Recognition

Conference paper

DOI: 10.1007/978-3-642-35230-0_11

Volume 198 of the book series Advances in Intelligent Systems and Computing (AISC)
Cite this paper as:
Coman H., Barth E., Martinetz T. (2013) Sparse Coding Neural Gas Applied to Image Recognition. In: Estévez P., Príncipe J., Zegers P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg

Abstract

A generalization of the Sparse Coding Neural Gas (SCNG) algorithm for feature learning is proposed and is then discussed in the context of modern classifier techniques for images. Two versions are presented. The latter obtains faster convergence by exploiting the nature of particular feature coding methods. The algorithm is then used as part of a larger image classification system, which is tested on the MNIST handwritten digit dataset and the NORB object dataset, obtaining results close to state-of-the-art methods.

Keywords

Neural Gas Sparse Coding Sparse Coding Neural Gas Image Recognition Matching Pursuit 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute for Neuro- and BioinformaticsUniversity of LübeckLübeckGermany
  2. 2.LAPIThe “POLITEHNICA” University of BucureştiBucureştiRomania