Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation

Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this proble...

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Bibliographic Details
Published in:Pattern recognition Vol. 121; p. 108201
Main Authors: Zhang, Hang, Li, Haili, Chen, Ning, Chen, Shengfeng, Liu, Jian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2022
Subjects:
Fuzzy clustering
Image segmentation
Spatial information
Variable filter window
Variable generalized neighbourhood window
Spatial information
Image segmentation
Fuzzy clustering
Variable generalized neighbourhood window
Variable filter window
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2021.108201