An Improved General Particle Swarm Optimization Algorithm for Fast Infrared Image Segmentation
The method of infrared image segmentation based on 2-D maximum fuzzy partition entropy is a typical integer programming problem with huge searching space and many local optima. In order to realize fast infrared image segmentation, an improved general particle swarm optimization algorithm is proposed...
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| creator | Ni Chao Li Qi Xia Liangzheng |
| description | The method of infrared image segmentation based on 2-D maximum fuzzy partition entropy is a typical integer programming problem with huge searching space and many local optima. In order to realize fast infrared image segmentation, an improved general particle swarm optimization algorithm is proposed. The algorithm is based on general particle swarm optimization, and it makes use of adaptive balance searching strategy. When the evolution stops, simulated annealing algorithm is introduced to select the current global optimum to be chaotic optimized for the sake of enhancing local searching ability and overcoming premature convergence. Experiment shows that the algorithm can get segmentation parameters quickly and accurately to realize fast infrared image segmentation. |
| doi_str_mv | 10.1109/CHICC.2006.4347264 |
| format | Conference Proceeding |
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Experiment shows that the algorithm can get segmentation parameters quickly and accurately to realize fast infrared image segmentation.</description><subject>2-D Maximum Fuzzy Partition Entropy</subject><subject>Chaos</subject><subject>Chaotic Optimization</subject><subject>Convergence</subject><subject>Entropy</subject><subject>General Particle Swarm optimization</subject><subject>Histograms</subject><subject>Image segmentation</subject><subject>Infrared Image Segmentation</subject><subject>Infrared imaging</subject><subject>Linear programming</subject><subject>Particle swarm optimization</subject><subject>Partitioning algorithms</subject><subject>Simulated annealing</subject><issn>1934-1768</issn><isbn>9787811240559</isbn><isbn>7811240556</isbn><isbn>7900719229</isbn><isbn>9787900719225</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNot0M1OAjEcBPAaNRGQF9BLX2Cx_373SDYCm5Bgol4lpbRYs90l3UajTy9RTnOZ3xwGoTsgMwBiHupVU9czSoicccYVlfwCjZUhRIGh1FyiqVFaaQDKiRDmCo3AMF6BkvoGjYfh4ySJATZCb_MON-mY-0-_x0vf-Wxb_GRzia71-PnL5oQ3xxJT_LEl9h2et4c-x_KecOgzXtih4KYL2eaTb5I9nJA_JN-Vv_otug62Hfz0nBP0unh8qVfVerNs6vm6iqBEqbhyKgRvtRROQFB7bggIx4TZOaOEZJoyF4RTRgZBpRZMcyoVsR52jDHOJuj-fzd677fHHJPN39vzN-wXKgNVuA</recordid><startdate>200707</startdate><enddate>200707</enddate><creator>Ni Chao</creator><creator>Li Qi</creator><creator>Xia Liangzheng</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200707</creationdate><title>An Improved General Particle Swarm Optimization Algorithm for Fast Infrared Image Segmentation</title><author>Ni Chao ; Li Qi ; Xia Liangzheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-47c7ffea865c51f7d49015c359bc97563823cf5c796f526853842670ae1b33343</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>2-D Maximum Fuzzy Partition Entropy</topic><topic>Chaos</topic><topic>Chaotic Optimization</topic><topic>Convergence</topic><topic>Entropy</topic><topic>General Particle Swarm optimization</topic><topic>Histograms</topic><topic>Image segmentation</topic><topic>Infrared Image Segmentation</topic><topic>Infrared imaging</topic><topic>Linear programming</topic><topic>Particle swarm optimization</topic><topic>Partitioning algorithms</topic><topic>Simulated annealing</topic><toplevel>online_resources</toplevel><creatorcontrib>Ni Chao</creatorcontrib><creatorcontrib>Li Qi</creatorcontrib><creatorcontrib>Xia Liangzheng</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ni Chao</au><au>Li Qi</au><au>Xia Liangzheng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Improved General Particle Swarm Optimization Algorithm for Fast Infrared Image Segmentation</atitle><btitle>2007 Chinese Control Conference</btitle><stitle>CHICC</stitle><date>2007-07</date><risdate>2007</risdate><spage>558</spage><epage>562</epage><pages>558-562</pages><issn>1934-1768</issn><isbn>9787811240559</isbn><isbn>7811240556</isbn><eisbn>7900719229</eisbn><eisbn>9787900719225</eisbn><abstract>The method of infrared image segmentation based on 2-D maximum fuzzy partition entropy is a typical integer programming problem with huge searching space and many local optima. In order to realize fast infrared image segmentation, an improved general particle swarm optimization algorithm is proposed. The algorithm is based on general particle swarm optimization, and it makes use of adaptive balance searching strategy. When the evolution stops, simulated annealing algorithm is introduced to select the current global optimum to be chaotic optimized for the sake of enhancing local searching ability and overcoming premature convergence. Experiment shows that the algorithm can get segmentation parameters quickly and accurately to realize fast infrared image segmentation.</abstract><pub>IEEE</pub><doi>10.1109/CHICC.2006.4347264</doi><tpages>5</tpages></addata></record> |
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| identifier | ISSN: 1934-1768 |
| ispartof | 2007 Chinese Control Conference, 2007, p.558-562 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | 2-D Maximum Fuzzy Partition Entropy Chaos Chaotic Optimization Convergence Entropy General Particle Swarm optimization Histograms Image segmentation Infrared Image Segmentation Infrared imaging Linear programming Particle swarm optimization Partitioning algorithms Simulated annealing |
| title | An Improved General Particle Swarm Optimization Algorithm for Fast Infrared Image Segmentation |
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