Sparse Coding Neural Gas Applied to Image Recognition

Horia Coman; Erhardt Barth; Thomas Martinetz

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

Sparse Coding Neural Gas Applied to Image Recognition

Horia Coman, Erhardt Barth, Thomas Martinetz

Institut für Neuro- und Bioinformatik

University POLITEHNICA of Bucureşti

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.

Originalsprache	Englisch
Titel	Advances in Self-Organizing Maps : 9th Workshop on Self-Organizing Maps
Redakteure/-innen	Pablo A. Estévez, José C. Príncipe, Pablo Zegers
Seitenumfang	10
Herausgeber (Verlag)	Springer Berlin Heidelberg
Erscheinungsdatum	01.01.2013
Seiten	105-114
ISBN (Print)	978-3-642-35229-4
ISBN (elektronisch)	978-3-642-35230-0
DOIs	https://doi.org/10.1007/978-3-642-35230-0_11
Publikationsstatus	Veröffentlicht - 01.01.2013
Veranstaltung	9th Workshop on Self-Organizing Maps - Santiago, Santiago, Chile Dauer: 12.12.2012 → 14.12.2012 http://www.die.uchile.cl/wsom2012/

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

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Coman, H., Barth, E., & Martinetz, T. (2013). Sparse Coding Neural Gas Applied to Image Recognition. in P. A. Estévez, J. C. Príncipe, & P. Zegers (Hrsg.), Advances in Self-Organizing Maps: 9th Workshop on Self-Organizing Maps (S. 105-114). (Advances in Intelligent Systems and Computing; Band Vol. 198). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_11

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title = "Sparse Coding Neural Gas Applied to Image Recognition",

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.",

author = "Horia Coman and Erhardt Barth and Thomas Martinetz",

year = "2013",

month = jan,

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doi = "10.1007/978-3-642-35230-0_11",

language = "English",

isbn = "978-3-642-35229-4",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Berlin Heidelberg",

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booktitle = "Advances in Self-Organizing Maps",

note = "9th Workshop on Self-Organizing Maps ; Conference date: 12-12-2012 Through 14-12-2012",

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Coman, H, Barth, E & Martinetz, T 2013, Sparse Coding Neural Gas Applied to Image Recognition. in PA Estévez, JC Príncipe & P Zegers (Hrsg.), Advances in Self-Organizing Maps: 9th Workshop on Self-Organizing Maps. Advances in Intelligent Systems and Computing, Bd. Vol. 198, Springer Berlin Heidelberg, S. 105-114, 9th Workshop on Self-Organizing Maps, Santiago, Chile, 12.12.12. https://doi.org/10.1007/978-3-642-35230-0_11

Sparse Coding Neural Gas Applied to Image Recognition. / Coman, Horia; Barth, Erhardt ; Martinetz, Thomas.
Advances in Self-Organizing Maps: 9th Workshop on Self-Organizing Maps. Hrsg. / Pablo A. Estévez; José C. Príncipe; Pablo Zegers. Springer Berlin Heidelberg, 2013. S. 105-114 (Advances in Intelligent Systems and Computing; Band Vol. 198).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Sparse Coding Neural Gas Applied to Image Recognition

AU - Coman, Horia

AU - Barth, Erhardt

AU - Martinetz, Thomas

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

UR - http://www.die.uchile.cl/wsom2012/

UR - https://link.springer.com/chapter/10.1007/978-3-642-35230-0_11

U2 - 10.1007/978-3-642-35230-0_11

DO - 10.1007/978-3-642-35230-0_11

M3 - Conference contribution

SN - 978-3-642-35229-4

T3 - Advances in Intelligent Systems and Computing

SP - 105

EP - 114

BT - Advances in Self-Organizing Maps

A2 - Estévez, Pablo A.

A2 - Príncipe, José C.

A2 - Zegers, Pablo

PB - Springer Berlin Heidelberg

T2 - 9th Workshop on Self-Organizing Maps

Y2 - 12 December 2012 through 14 December 2012

ER -

Sparse Coding Neural Gas Applied to Image Recognition

Abstract

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