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Proceedings Paper

Design of an adaptive genetic learning neural network system for image compression
Author(s): Jianmin Jiang; Darren Butler
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Paper Abstract

In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neural networks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.

Paper Details

Date Published: 1 April 1997
PDF: 8 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269778
Show Author Affiliations
Jianmin Jiang, Loughborough Univ. (United Kingdom)
Darren Butler, Bolton Institute (United Kingdom)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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