Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering
Due to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation,which resulted in the low clustering precision,poor retention of details,and large time complexity,an intuitionistic fuzzy c-means clustering algorithm wa...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-05-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020071/ |
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| author | Xiaofei WANG Fankui HU Shuo HUANG |
| author_facet | Xiaofei WANG Fankui HU Shuo HUANG |
| author_sort | Xiaofei WANG |
| collection | DOAJ |
| description | Due to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation,which resulted in the low clustering precision,poor retention of details,and large time complexity,an intuitionistic fuzzy c-means clustering algorithm was proposed based on spatial distribution information suitable for infrared image segmentation of power equipment.The non-target objects with high intensity and the non-uniformity of image intensity in the infrared image had strong interference to the image segmentation,which could be effectively suppressed by the proposed algorithm.Firstly,the Gaussian model was introduced into the global spatial distribution information of power equipment to improve the IFCM algorithm.Secondly,the membership function was optimized by local spatial operator to solve the problem of edge blur and image intensity inhomogeneity.The experiments conducted on Terravic motion IR database and the data set containing 300 infrared images of power equipment show that,the relative region error rate is about 10% and is less affected by the change of fuzzy factor m.The effectiveness and applicability of the proposed algorithm are superior to other comparison algorithms. |
| format | Article |
| id | doaj-art-65661844e61949ee94ed97c1a2cd116d |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-65661844e61949ee94ed97c1a2cd116d2025-01-14T07:19:19ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-05-014112012959735592Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clusteringXiaofei WANGFankui HUShuo HUANGDue to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation,which resulted in the low clustering precision,poor retention of details,and large time complexity,an intuitionistic fuzzy c-means clustering algorithm was proposed based on spatial distribution information suitable for infrared image segmentation of power equipment.The non-target objects with high intensity and the non-uniformity of image intensity in the infrared image had strong interference to the image segmentation,which could be effectively suppressed by the proposed algorithm.Firstly,the Gaussian model was introduced into the global spatial distribution information of power equipment to improve the IFCM algorithm.Secondly,the membership function was optimized by local spatial operator to solve the problem of edge blur and image intensity inhomogeneity.The experiments conducted on Terravic motion IR database and the data set containing 300 infrared images of power equipment show that,the relative region error rate is about 10% and is less affected by the change of fuzzy factor m.The effectiveness and applicability of the proposed algorithm are superior to other comparison algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020071/intuitionistic fuzzy c-means clusteringinfrared imageGaussian modellocal information |
| spellingShingle | Xiaofei WANG Fankui HU Shuo HUANG Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering Tongxin xuebao intuitionistic fuzzy c-means clustering infrared image Gaussian model local information |
| title | Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering |
| title_full | Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering |
| title_fullStr | Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering |
| title_full_unstemmed | Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering |
| title_short | Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering |
| title_sort | infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c means clustering |
| topic | intuitionistic fuzzy c-means clustering infrared image Gaussian model local information |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020071/ |
| work_keys_str_mv | AT xiaofeiwang infraredimagesegmentationalgorithmbasedondistributioninformationintuitionisticfuzzycmeansclustering AT fankuihu infraredimagesegmentationalgorithmbasedondistributioninformationintuitionisticfuzzycmeansclustering AT shuohuang infraredimagesegmentationalgorithmbasedondistributioninformationintuitionisticfuzzycmeansclustering |