Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation
Fuzzy local information clustering is the most widely robust segmentation methods, but it is only suitable for image corrupted by certain intensity noise. Later, although fuzzy local information clustering integrated guided filter is improved the ability of suppressing noise, it still cannot meet th...
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| Veröffentlicht in: | Multimedia tools and applications 2022-08, Vol.81 (20), p.28431-28477 |
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| container_title | Multimedia tools and applications |
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| creator | Qiao, CaiCai Wu, ChengMao Li, ChangXing Wang, JiaYe |
| description | Fuzzy local information clustering is the most widely robust segmentation methods, but it is only suitable for image corrupted by certain intensity noise. Later, although fuzzy local information clustering integrated guided filter is improved the ability of suppressing noise, it still cannot meet the needs of image with high noise. This paper proposed a novel robust fuzzy local information clustering combined kernel metric with guided filter. Firstly, guided filter is introduced into fuzzy local information clustering with kernel metric (KWFLICM), and a novel multiple objective optimization model for fuzzy clustering is constructed. Secondly, the optimization model is solved by Lagrange multiplier method, and the iterative algorithm for image segmentation is presented. Experimental results show that the proposed algorithm has better segmentation performance and robustness than existing state of the art guided filter-driven fuzzy clustering with local information. |
| doi_str_mv | 10.1007/s11042-022-12840-0 |
| format | Article |
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Later, although fuzzy local information clustering integrated guided filter is improved the ability of suppressing noise, it still cannot meet the needs of image with high noise. This paper proposed a novel robust fuzzy local information clustering combined kernel metric with guided filter. Firstly, guided filter is introduced into fuzzy local information clustering with kernel metric (KWFLICM), and a novel multiple objective optimization model for fuzzy clustering is constructed. Secondly, the optimization model is solved by Lagrange multiplier method, and the iterative algorithm for image segmentation is presented. Experimental results show that the proposed algorithm has better segmentation performance and robustness than existing state of the art guided filter-driven fuzzy clustering with local information.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-022-12840-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Clustering ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Image segmentation ; Iterative algorithms ; Iterative methods ; Kernels ; Lagrange multiplier ; Multimedia ; Multimedia Information Systems ; Multiple objective analysis ; Neighborhoods ; Noise intensity ; Optimization models ; Robustness (mathematics) ; Special Purpose and Application-Based Systems</subject><ispartof>Multimedia tools and applications, 2022-08, Vol.81 (20), p.28431-28477</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-ebb31221a0465a6e59ae3a71009c05d17baec2e92ef192a1ec932bc2c16384613</citedby><cites>FETCH-LOGICAL-c249t-ebb31221a0465a6e59ae3a71009c05d17baec2e92ef192a1ec932bc2c16384613</cites><orcidid>0000-0003-1811-7218</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-022-12840-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-022-12840-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Qiao, CaiCai</creatorcontrib><creatorcontrib>Wu, ChengMao</creatorcontrib><creatorcontrib>Li, ChangXing</creatorcontrib><creatorcontrib>Wang, JiaYe</creatorcontrib><title>Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>Fuzzy local information clustering is the most widely robust segmentation methods, but it is only suitable for image corrupted by certain intensity noise. 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| subjects | Algorithms Clustering Computer Communication Networks Computer Science Data Structures and Information Theory Image segmentation Iterative algorithms Iterative methods Kernels Lagrange multiplier Multimedia Multimedia Information Systems Multiple objective analysis Neighborhoods Noise intensity Optimization models Robustness (mathematics) Special Purpose and Application-Based Systems |
| title | Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation |
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