Color image segmentation based on multiobjective artificial bee colony optimization

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dc.contributor.authorSag, Tahir
dc.contributor.authorCunkas, Mehmet
dc.date.accessioned2020-03-26T19:01:24Z
dc.date.available2020-03-26T19:01:24Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Scientific Research Coordination (BAP)Selcuk Universityen_US
dc.description.sponsorshipSelcuk University Scientific Research Coordination (BAP) is acknowledged for its contribution to the financial support of this work.en_US
dc.identifier.doi10.1016/j.asoc.2015.05.016en_US
dc.identifier.endpage401en_US
dc.identifier.issn1568-4946en_US
dc.identifier.issn1872-9681en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage389en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.asoc.2015.05.016
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31938
dc.identifier.volume34en_US
dc.identifier.wosWOS:000357469500030en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofAPPLIED SOFT COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectColor image segmentationen_US
dc.subjectMultiobjective optimizationen_US
dc.subjectArtificial bee colonyen_US
dc.subjectFuzzy c-meansen_US
dc.titleColor image segmentation based on multiobjective artificial bee colony optimizationen_US
dc.typeArticleen_US

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