research-article

Pythagorean fuzzy C‐means algorithm for image segmentation

Authors:

Guangcheng Song,

Zeshui XuAuthors Info & Claims

International Journal of Intelligent Systems, Volume 36, Issue 3

Pages 1223 - 1243

https://doi.org/10.1002/int.22339

Published: 28 January 2021 Publication History

Abstract

In recent decades, image segmentation has aroused great interest of many researchers, and has become an important part of machine learning, pattern recognition, and computer vision. Among many methods of image segmentation, fuzzy C‐means (FCM) algorithm is undoubtedly a milestone in unsupervised method. With the further study of FCM, various different kinds of FCM algorithms are put forward to deal with the specific problems in image segmentation. Because there exist uncertainties in different regions of the image and similarity in the same region, reducing the uncertainty is still the main problem in image segmentation. Considering that Pythagorean fuzzy set (PFS) is a powerful tool to deal with uncertainty, in this paper, we use PFS to describe the uncertainty of image segmentation, including introducing fuzzification and defuzzification process and Pythagorean fuzzy element to describe the membership degree of pixel, combine the neighborhood information with weights and Pythagorean fuzzy distance, and propose Pythagorean fuzzy C‐means (PFCM) algorithm. Finally, we apply PFCM algorithm in image segmentation, such as different size images and Berkeley Segmentation Data Set to illustrate the effectiveness and applicability of our proposed algorithm. Meanwhile, we do comparison analysis between PFCM, fully convolution network and Deep‐image‐Prior networks, these results show that our proposed PFCM has good intuition and effectiveness.

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cover image International Journal of Intelligent Systems

International Journal of Intelligent Systems Volume 36, Issue 3

March 2021

355 pages

ISSN:0884-8173

DOI:10.1002/int.v36.3

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© 2020 Wiley Periodicals LLC.

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 28 January 2021

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Cited By

Zhou JHuang CGao CWang YShen XWu X(2024)Weighted Subspace Fuzzy Clustering with Adaptive ProjectionInternational Journal of Intelligent Systems10.1155/2024/66967752024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/6696775
Zhu CJiang WZhou WXiao H(2022)Similarity Retrieval Based on Image Background AnalysisInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.30942614:1(1-14)Online publication date: 23-Sep-2022
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Ji CZhu ZWang XZhai WZong XChen AZhou M(2022)Task‐aware swapping for efficient DNN inference on DRAM‐constrained edge systemsInternational Journal of Intelligent Systems10.1002/int.2293337:10(8155-8169)Online publication date: 25-Aug-2022
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Shi ZChang CChen HDu XZhang H(2022)PR‐NETInternational Journal of Intelligent Systems10.1002/int.2282237:5(3166-3188)Online publication date: 14-Jan-2022
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Yang ZLin MLi YZhou WXu B(2021)Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithmInternational Journal of Intelligent Systems10.1002/int.2255436:11(6387-6418)Online publication date: 24-Sep-2021
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