Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison

Four Radial Basis Network architectures are evaluated for their performance in terms of classification accuracy and computation time. The architectures are Radial Basis Neural Network, Goal Oriented Radial Basis Architecture, Generalized Gaussian Network, Probabilistic Neural Network. Zemike Invaria...

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Main Authors: Saad, Puteh, Ibrahim, Subariah, Mahshos, Nur Safawati
Format: Article
Language:English
Published: Penerbit UTM Press 2008
Subjects:
Online Access:http://eprints.utm.my/10701/
http://eprints.utm.my/10701/1/PutehSaad2008_ClassificationOfAircraftImageUsingDifferent.pdf
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author Saad, Puteh
Ibrahim, Subariah
Mahshos, Nur Safawati
author_facet Saad, Puteh
Ibrahim, Subariah
Mahshos, Nur Safawati
author_sort Saad, Puteh
building UTeM Institutional Repository
collection Online Access
description Four Radial Basis Network architectures are evaluated for their performance in terms of classification accuracy and computation time. The architectures are Radial Basis Neural Network, Goal Oriented Radial Basis Architecture, Generalized Gaussian Network, Probabilistic Neural Network. Zemike Invariant Moment is utilized to extract a set of features from the aircraft image. Each of the architectures is used to'classify the image feature vectors. It is found that Generalized Gaussian Neural Network Architecture portrays perfect classification of 100% at a fastest time. Hence, the Generalized Gaussian Neural Network Architecture has a high potential to be adopted to classify images in a real-time environment.
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publishDate 2008
publisher Penerbit UTM Press
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spelling utm-107012017-11-01T04:17:23Z http://eprints.utm.my/10701/ Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison Saad, Puteh Ibrahim, Subariah Mahshos, Nur Safawati QA76 Computer software Four Radial Basis Network architectures are evaluated for their performance in terms of classification accuracy and computation time. The architectures are Radial Basis Neural Network, Goal Oriented Radial Basis Architecture, Generalized Gaussian Network, Probabilistic Neural Network. Zemike Invariant Moment is utilized to extract a set of features from the aircraft image. Each of the architectures is used to'classify the image feature vectors. It is found that Generalized Gaussian Neural Network Architecture portrays perfect classification of 100% at a fastest time. Hence, the Generalized Gaussian Neural Network Architecture has a high potential to be adopted to classify images in a real-time environment. Penerbit UTM Press 2008-12 Article PeerReviewed application/pdf en http://eprints.utm.my/10701/1/PutehSaad2008_ClassificationOfAircraftImageUsingDifferent.pdf Saad, Puteh and Ibrahim, Subariah and Mahshos, Nur Safawati (2008) Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison. Jurnal Teknologi Maklumat, 20 (4). pp. 1-16. ISSN 0128-3790
spellingShingle QA76 Computer software
Saad, Puteh
Ibrahim, Subariah
Mahshos, Nur Safawati
Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title_full Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title_fullStr Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title_full_unstemmed Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title_short Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
title_sort classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
topic QA76 Computer software
url http://eprints.utm.my/10701/
http://eprints.utm.my/10701/1/PutehSaad2008_ClassificationOfAircraftImageUsingDifferent.pdf