Alomoush, W, Alrosan, A, Khashan, Osama A., Houssein, Essam H., Attar, Hani, Alweshah, Mohammed and Alhosban, Fuad (2022) Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation. Sensors, 22. ISSN 1424-8220
22/22/8956 - Published Version
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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.
Affiliation: | Skyline University College |
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SUC Author(s): | Alomoush, W ORCID: https://orcid.org/0000-0002-2937-4327 and Alrosan, A ORCID: https://orcid.org/0000-0001-9400-4077 |
All Author(s): | Alomoush, W, Alrosan, A, Khashan, Osama A., Houssein, Essam H., Attar, Hani, Alweshah, Mohammed and Alhosban, Fuad |
Item Type: | Article |
Uncontrolled Keywords: | data clustering, artificial bee colony, centroids location, natural images, validity index |
Subjects: | B Information Technology > BD Big Data Analitics B Information Technology > BQ Data Analytics |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:57 |
Last Modified: | 25 Dec 2023 13:57 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/613 |
Publisher URL: | http://dx.doi.org/10.3390/s22228956 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/17524 |
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