Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation
ISSN: 0264-4401
Article publication date: 2 January 2024
Issue publication date: 4 March 2024
Abstract
Purpose
Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.
Design/methodology/approach
The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.
Findings
The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.
Originality/value
Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.
Keywords
Acknowledgements
The authors would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, the authors would like to thank the School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China, for funding this study.
Citation
Cai, X., Yang, X. and Wu, C. (2024), "Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation", Engineering Computations, Vol. 41 No. 1, pp. 107-130. https://doi.org/10.1108/EC-08-2023-0403
Publisher
:Emerald Publishing Limited
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