Harmony search-based fuzzy clustering algorithms for image segmentation.

Algoritma-algoritma pengkelompokan kabur, yang tergolong di dalam kategori pembelajaran mesin tanpa selia, adalah di antara kaedah segmentasi imej yang paling berjaya. Namun demikian, terdapat dua isu utama yang membataskan keberkesanan kaedah ini: kepekaan terhadap pemilihan pusat kelompok permulaa...

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Bibliographic Details
Main Author:	Alia, Osama Moh’d Radi
Format:	Thesis
Language:	English
Published:	2011
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf
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id	my-usm-ep.42978
record_format	uketd_dc
spelling	my-usm-ep.429782018-11-22T04:56:44Z Harmony search-based fuzzy clustering algorithms for image segmentation. 2011-02 Alia, Osama Moh’d Radi QA75.5-76.95 Electronic computers. Computer science Algoritma-algoritma pengkelompokan kabur, yang tergolong di dalam kategori pembelajaran mesin tanpa selia, adalah di antara kaedah segmentasi imej yang paling berjaya. Namun demikian, terdapat dua isu utama yang membataskan keberkesanan kaedah ini: kepekaan terhadap pemilihan pusat kelompok permulaan dan ketidakpastian terhadap bilangan kelompok sebenar di dalam set data. Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset. 2011-02 Thesis http://eprints.usm.my/42978/ http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Alia, Osama Moh’d Radi Harmony search-based fuzzy clustering algorithms for image segmentation.
description	Algoritma-algoritma pengkelompokan kabur, yang tergolong di dalam kategori pembelajaran mesin tanpa selia, adalah di antara kaedah segmentasi imej yang paling berjaya. Namun demikian, terdapat dua isu utama yang membataskan keberkesanan kaedah ini: kepekaan terhadap pemilihan pusat kelompok permulaan dan ketidakpastian terhadap bilangan kelompok sebenar di dalam set data. Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. However, two main issues plague these clustering algorithms: initialization sensitivity of cluster centers and unknown number of actual clusters in the given dataset.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alia, Osama Moh’d Radi
author_facet	Alia, Osama Moh’d Radi
author_sort	Alia, Osama Moh’d Radi
title	Harmony search-based fuzzy clustering algorithms for image segmentation.
title_short	Harmony search-based fuzzy clustering algorithms for image segmentation.
title_full	Harmony search-based fuzzy clustering algorithms for image segmentation.
title_fullStr	Harmony search-based fuzzy clustering algorithms for image segmentation.
title_full_unstemmed	Harmony search-based fuzzy clustering algorithms for image segmentation.
title_sort	harmony search-based fuzzy clustering algorithms for image segmentation.
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2011
url	http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf
_version_	1747821140831633408

Harmony search-based fuzzy clustering algorithms for image segmentation.

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