A multimodal particle swarm optimization-based approach for image segmentation

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dc.contributor.authorFarshi, Taymaz Rahkar
dc.contributor.authorDrake, John H.
dc.contributor.authorÖzcan, Ender
dc.date.accessioned2021-05-15T11:33:35Z
dc.date.available2021-05-15T11:33:35Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.descriptionOzcan, Ender/0000-0003-0276-1391; Rahkar-Farshi, Taymaz/0000-0003-4070-1058; Ozcan, Ender/0000-0003-0276-1391
dc.description.abstractColor image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three successive stages. In the first stage, a three-dimensional histogram of pixel colors based on the RGB model is smoothened using a Gaussian filter. This process helps to eliminate unreliable and non-dominating peaks that are too close to one another in the histogram. In the next stage, the peaks representing different clusters in the histogram are identified using a multimodal particle swarm optimization algorithm. Finally, pixels are assigned to the most appropriate cluster based on Euclidean distance. Determining the number of clusters to be used is often a manual process left for a user and represents a challenge for various segmentation algorithms. The proposed method is designed to determine an appropriate number of clusters, in addition to the actual peaks, automatically. Experiments confirm that the proposed approach yields desirable results, demonstrating that it can find an appropriate set of clusters for a set of well-known benchmark images. (C) 2020 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2020.113233
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85079045220
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2020.113233
dc.identifier.urihttps://hdl.handle.net/20.500.12939/191
dc.identifier.volume149en_US
dc.identifier.wosWOS:000525819400008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorFarshi, Taymaz Rahkar
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectColor Image Segmentationen_US
dc.subjectClusteringen_US
dc.subjectParticle Swarm Optimisationen_US
dc.subjectMultimodal Optimisationen_US
dc.titleA multimodal particle swarm optimization-based approach for image segmentation
dc.typeArticle

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