Multi-Threshold Infrared Image Segmentation Based on the Modified Particle Swarm Optimization Algorithm
Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the c...
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| creator | Yi-Tong Liu Ming-Yin Fu Hong-Bin Gao |
| description | Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly. |
| doi_str_mv | 10.1109/ICMLC.2007.4370174 |
| format | Conference Proceeding |
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This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. 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The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.</description><subject>Automation</subject><subject>Cybernetics</subject><subject>Data mining</subject><subject>Entropy</subject><subject>Image segmentation</subject><subject>Information science</subject><subject>Infrared image segmentation</subject><subject>Infrared imaging</subject><subject>Machine learning</subject><subject>Machine learning algorithms</subject><subject>Multi-threshold</subject><subject>Particle swarm optimization</subject><subject>Particle swarm optimization algorithm</subject><issn>2160-133X</issn><isbn>1424409721</isbn><isbn>9781424409723</isbn><isbn>9781424409730</isbn><isbn>142440973X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UF1Lw0AQPFHBWvMH9CV_IHX3PnK5xxr8KKRUsIJv5ZJckpOkKZcT0V_vQevTzuzsDMwScouwQAR1v8rXRb6gAHLBmQSU_IxESmbIKeegJINzcv1PKF6QGcUUEmTs44pE0_QJEEwpB8pmpF1_9d4m286ZqRv7Ol7tG6edCWDQrYnfTDuYvdfejvv4QU9BCMB3Jl6PtW1s4K_aeVv14fZbuyHeHLwd7O_Rsezb0VnfDTfkstH9ZKLTnJP3p8dt_pIUm-dVviwSi1L4pErrDERTc5Qqq5WUok5Vk5UQtppDWYIOxVNTSqmoYJohFamUZVUKFA0im5O7Y641xuwOzg7a_exOf2J_nz9Z5g</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Yi-Tong Liu</creator><creator>Ming-Yin Fu</creator><creator>Hong-Bin Gao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>Multi-Threshold Infrared Image Segmentation Based on the Modified Particle Swarm Optimization Algorithm</title><author>Yi-Tong Liu ; Ming-Yin Fu ; Hong-Bin Gao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c6d805fd41798d9775d69f8b0805a40bb0a2006eb779253a3125677bcb515f113</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Automation</topic><topic>Cybernetics</topic><topic>Data mining</topic><topic>Entropy</topic><topic>Image segmentation</topic><topic>Information science</topic><topic>Infrared image segmentation</topic><topic>Infrared imaging</topic><topic>Machine learning</topic><topic>Machine learning algorithms</topic><topic>Multi-threshold</topic><topic>Particle swarm optimization</topic><topic>Particle swarm optimization algorithm</topic><toplevel>online_resources</toplevel><creatorcontrib>Yi-Tong Liu</creatorcontrib><creatorcontrib>Ming-Yin Fu</creatorcontrib><creatorcontrib>Hong-Bin Gao</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 Xplore</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>Yi-Tong Liu</au><au>Ming-Yin Fu</au><au>Hong-Bin Gao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multi-Threshold Infrared Image Segmentation Based on the Modified Particle Swarm Optimization Algorithm</atitle><btitle>2007 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2007-08</date><risdate>2007</risdate><volume>1</volume><spage>383</spage><epage>388</epage><pages>383-388</pages><issn>2160-133X</issn><isbn>1424409721</isbn><isbn>9781424409723</isbn><eisbn>9781424409730</eisbn><eisbn>142440973X</eisbn><abstract>Threshold extraction is the fundamental step in multi-threshold image segmentation. This paper has introduced particle swarm optimization algorithm (PSO) for threshold extraction. But when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called premature. This can cause image segmentation failure. This paper proposes a modified particle swarm optimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold extraction. Simulation results show that the MPSO has better performance and quicker speed. The experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2007.4370174</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2007 International Conference on Machine Learning and Cybernetics, 2007, Vol.1, p.383-388 |
| issn | 2160-133X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automation Cybernetics Data mining Entropy Image segmentation Information science Infrared image segmentation Infrared imaging Machine learning Machine learning algorithms Multi-threshold Particle swarm optimization Particle swarm optimization algorithm |
| title | Multi-Threshold Infrared Image Segmentation Based on the Modified Particle Swarm Optimization Algorithm |
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