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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.

Details

Title
Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation
Author
Alomoush, Waleed 1   VIAFID ORCID Logo  ; Khashan, Osama A 2   VIAFID ORCID Logo  ; Alrosan, Ayat 1 ; Houssein, Essam H 3   VIAFID ORCID Logo  ; Attar, Hani 4   VIAFID ORCID Logo  ; Alweshah, Mohammed 5 ; Alhosban, Fuad 6   VIAFID ORCID Logo 

 School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates 
 Research and Innovation Centers, Rabdan Academy, Abu Dhabi P.O. Box 114646, United Arab Emirates 
 Faculty of Computers and Information, Minia University, Minia 61519, Egypt 
 Department of Energy Engineering, Zarqa University, Zarqa 13132, Jordan 
 Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan 
 CIS Department, Faculty of Computer Information Systems, Higher Colleges of Technology, Dubai P.O. Box 16062, United Arab Emirates 
First page
8956
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2739462692
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.