Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to sol...

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Bibliographic Details
Published in2006 International Symposium on Intelligent Signal Processing and Communications [sic.] : Yonago, Japan, December 12-15, 2006 pp. 538 - 541
Main Authors Nobuhara, H., Chang-Wook Han
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2006
Subjects
Algebra
Evolutionary computation
Genetic algorithms
Image coding
Image processing
Image reconstruction
Learning systems
Multi-layer neural network
Neural networks
Optimization methods
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ISBN9780780397323
0780397320
DOI10.1109/ISPACS.2006.364715

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Summary:A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is proposed. Through the image compression/reconstruction experiment using test images extracted from standard image database (SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed learning method is better than that obtained by the conventional method.
ISBN:9780780397323
0780397320
DOI:10.1109/ISPACS.2006.364715