Harmony search-based fuzzy clustering algorithms for image segmentation.

Alia, Osama Moh’d Radi (2011) Harmony search-based fuzzy clustering algorithms for image segmentation. ["eprint_fieldopt_thesis_type_phd" not defined] thesis, Universiti Sains Malaysia.

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Abstract

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.

Item Type: Thesis (["eprint_fieldopt_thesis_type_phd" not defined])
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis
Depositing User: Mr Erwan Roslan
Date Deposited: 22 Nov 2018 04:56
Last Modified: 22 Nov 2018 04:56
URI: http://eprints.usm.my/id/eprint/42978

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