A new image segmentation method based on the ICSO-ISPCNN model
To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dyn...
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| Veröffentlicht in: | Multimedia tools and applications 2020-10, Vol.79 (37-38), p.28131-28154 |
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| container_title | Multimedia tools and applications |
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| creator | Liang, Jianhui Wang, Lifang Ma, Miao |
| description | To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm. |
| doi_str_mv | 10.1007/s11042-019-08596-9 |
| format | Article |
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First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-019-08596-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Ant colony optimization ; Chicks ; Computer Communication Networks ; Computer Science ; Data Structures and Information Theory ; Entropy (Information theory) ; Genetic algorithms ; Image segmentation ; Mathematical models ; Multimedia Information Systems ; Neural networks ; Optimization algorithms ; Parameters ; Special Purpose and Application-Based Systems</subject><ispartof>Multimedia tools and applications, 2020-10, Vol.79 (37-38), p.28131-28154</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-c077062821f26e7a1bc470daf82232d20e0a4b2360b81daa21a65213d65853993</citedby><cites>FETCH-LOGICAL-c319t-c077062821f26e7a1bc470daf82232d20e0a4b2360b81daa21a65213d65853993</cites><orcidid>0000-0003-4745-757X</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/s11042-019-08596-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-019-08596-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Liang, Jianhui</creatorcontrib><creatorcontrib>Wang, Lifang</creatorcontrib><creatorcontrib>Ma, Miao</creatorcontrib><title>A new image segmentation method based on the ICSO-ISPCNN model</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. 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| subjects | Ant colony optimization Chicks Computer Communication Networks Computer Science Data Structures and Information Theory Entropy (Information theory) Genetic algorithms Image segmentation Mathematical models Multimedia Information Systems Neural networks Optimization algorithms Parameters Special Purpose and Application-Based Systems |
| title | A new image segmentation method based on the ICSO-ISPCNN model |
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