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Image Compression with Self-Organizing Networks

Freisleben, Bernd ; Mengel, Maximilian (1993)
Image Compression with Self-Organizing Networks.
New Trends in Neural Computation. Proceedings.
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

In this paper we evaluate and compare the performance of self-organizing neural networks applied to the task of image compression. The networks investigated are two-layered architectures with linear neurons, and variants of Hebbian learning rules are used to reduce the dimensionality of the inputs while preserving a maximum of information in the output units. Although in theory all networks considered are effectively equivalent to performing the Karhunen-Loeve transform, which is the optimal image compression method in the sense that it allows linear reconstruction of the input information with minimal squared error, the results obtained in practice reveal significant differences between the networks. An experimental study has been conducted to demonstrate these differences and thus some light is shed on the suitability of self-organizing neural networks for image compression, particularly in comparison to more conventional methods.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 1993
Autor(en): Freisleben, Bernd ; Mengel, Maximilian
Art des Eintrags: Bibliographie
Titel: Image Compression with Self-Organizing Networks
Sprache: Deutsch
Publikationsjahr: 1993
Verlag: Springer, Berlin, Heidelberg
Reihe: Lecture Notes in Computer Science (LNCS); 686
Veranstaltungstitel: New Trends in Neural Computation. Proceedings
Kurzbeschreibung (Abstract):

In this paper we evaluate and compare the performance of self-organizing neural networks applied to the task of image compression. The networks investigated are two-layered architectures with linear neurons, and variants of Hebbian learning rules are used to reduce the dimensionality of the inputs while preserving a maximum of information in the output units. Although in theory all networks considered are effectively equivalent to performing the Karhunen-Loeve transform, which is the optimal image compression method in the sense that it allows linear reconstruction of the input information with minimal squared error, the results obtained in practice reveal significant differences between the networks. An experimental study has been conducted to demonstrate these differences and thus some light is shed on the suitability of self-organizing neural networks for image compression, particularly in comparison to more conventional methods.

Freie Schlagworte: Hebbian learning for image compression, Principal component analysis, Self-organizing networks
Fachbereich(e)/-gebiet(e): nicht bekannt
20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 16 Apr 2018 09:09
Letzte Änderung: 16 Apr 2018 09:09
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