Interval possibilistic C-means algorithm and its application in image segmentation
Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results...
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| Published in: | Information sciences Vol. 612; pp. 465 - 480 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2022
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. |
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| AbstractList | Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. |
| Author | Zeng, Wenyi Ma, Rong Liu, Yuqing Cui, Hanshuai Xu, Zeshui |
| Author_xml | – sequence: 1 givenname: Wenyi surname: Zeng fullname: Zeng, Wenyi organization: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China – sequence: 2 givenname: Yuqing surname: Liu fullname: Liu, Yuqing organization: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China – sequence: 3 givenname: Hanshuai surname: Cui fullname: Cui, Hanshuai email: cuihanshuai@126.com organization: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China – sequence: 4 givenname: Rong surname: Ma fullname: Ma, Rong email: macrosse@163.com organization: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China – sequence: 5 givenname: Zeshui surname: Xu fullname: Xu, Zeshui organization: Business School, Sichuan University, Chengdu 610064, China |
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| Keywords | Interval value Image segmentation Possibilistic C-means algorithm Fuzzy C-means algorithm Information fusion |
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