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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| Format: | Thesis |
| Language: | English |
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2011
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| Online Access: | http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf |
| _version_ | 1825834719595462656 |
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| author | Alia, Osama Moh’d Radi |
| author_facet | Alia, Osama Moh’d Radi |
| author_sort | Alia, Osama Moh’d Radi |
| collection | USM |
| 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.
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| first_indexed | 2024-03-06T15:26:55Z |
| format | Thesis |
| id | usm.eprints-42978 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:26:55Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | usm.eprints-429782018-11-22T04:56:44Z http://eprints.usm.my/42978/ Harmony search-based fuzzy clustering algorithms for image segmentation. 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 NonPeerReviewed application/pdf en http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf Alia, Osama Moh’d Radi (2011) Harmony search-based fuzzy clustering algorithms for image segmentation. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Alia, Osama Moh’d Radi Harmony search-based fuzzy clustering algorithms for image segmentation. |
| title | 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_short | Harmony search-based fuzzy clustering algorithms for image segmentation. |
| title_sort | harmony search based fuzzy clustering algorithms for image segmentation |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/42978/1/Pages_from_HARMONY_SEARCH-BASED_FUZZY.pdf |
| work_keys_str_mv | AT aliaosamamohdradi harmonysearchbasedfuzzyclusteringalgorithmsforimagesegmentation |