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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Veröffentlicht in:Information sciences Jg. 612; S. 465 - 480
Hauptverfasser: Zeng, Wenyi, Liu, Yuqing, Cui, Hanshuai, Ma, Rong, Xu, Zeshui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.10.2022
Schlagworte:
Fuzzy C-means algorithm
Image segmentation
Information fusion
Interval value
Possibilistic C-means algorithm
Interval value
Image segmentation
Possibilistic C-means algorithm
Fuzzy C-means algorithm
Information fusion
ISSN:0020-0255, 1872-6291
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Beschreibung
Zusammenfassung: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.08.082