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Article: Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

TitleManta ray foraging optimizer-based image segmentation with a two-strategy enhancement
Authors
KeywordsImage processing
Manta ray foraging optimization
Metaheuristic
Multilevel thresholding
Oppositional learning
Vertical crossover search
Issue Date12-Jan-2023
PublisherElsevier
Citation
Knowledge-Based Systems, 2023, v. 262 How to Cite?
Abstract

Image processing is an evolving field that calls for more powerful techniques to extract useful information from images. In particular, image segmentation is a preprocessing step that helps separate objects in a digital image. This article introduces an enhanced manta ray foraging optimizer (MRFO) based on two strategies - oppositional learning (OL) and vertical crossover (VC) search - for color image segmentation. This combination technique focuses on the enhancement of the explorative and exploitative cores, without compromising the computational speed. The proposed algorithm, termed OL-MRFO-VC, is integrated with Kapur entropy to identify the best threshold configuration in each image component (RGB). The technique is tested over three datasets consisting of different scenes. The threshold vector consists of both lower and higher levels in the experiments. In addition, OL-MRFO-VC is compared with fourteen competitive metaheuristics, and eleven measures are used to evaluate their performance quantitatively and qualitatively. According to the computational results, our proposed method outperforms state-of-the-art techniques, especially in the higher threshold levels. Furthermore, the p values in the Wilcoxon signed-rank test confirm a significant improvement brought by our proposed method, suggesting a superior capability of OL-MRFO-VC for solving image segmentation problems.


Persistent Identifierhttp://hdl.handle.net/10722/331199
ISSN
2023 Impact Factor: 7.2
2023 SCImago Journal Rankings: 2.219
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, BJ-
dc.contributor.authorPereira, JLJ-
dc.contributor.authorOliva, D-
dc.contributor.authorLiu, S-
dc.contributor.authorKuo, YH-
dc.date.accessioned2023-09-21T06:53:38Z-
dc.date.available2023-09-21T06:53:38Z-
dc.date.issued2023-01-12-
dc.identifier.citationKnowledge-Based Systems, 2023, v. 262-
dc.identifier.issn0950-7051-
dc.identifier.urihttp://hdl.handle.net/10722/331199-
dc.description.abstract<p>Image processing is an evolving field that calls for more powerful techniques to extract useful information from images. In particular, image segmentation is a preprocessing step that helps separate objects in a digital image. This article introduces an enhanced manta ray foraging optimizer (MRFO) based on two strategies - oppositional learning (OL) and vertical crossover (VC) search - for color image segmentation. This combination technique focuses on the enhancement of the explorative and exploitative cores, without compromising the computational speed. The proposed algorithm, termed OL-MRFO-VC, is integrated with Kapur entropy to identify the best threshold configuration in each image component (RGB). The technique is tested over three datasets consisting of different scenes. The threshold vector consists of both lower and higher levels in the experiments. In addition, OL-MRFO-VC is compared with fourteen competitive metaheuristics, and eleven measures are used to evaluate their performance quantitatively and qualitatively. According to the computational results, our proposed method outperforms state-of-the-art techniques, especially in the higher threshold levels. Furthermore, the p values in the Wilcoxon signed-rank test confirm a significant improvement brought by our proposed method, suggesting a superior capability of OL-MRFO-VC for solving image segmentation problems.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofKnowledge-Based Systems-
dc.subjectImage processing-
dc.subjectManta ray foraging optimization-
dc.subjectMetaheuristic-
dc.subjectMultilevel thresholding-
dc.subjectOppositional learning-
dc.subjectVertical crossover search-
dc.titleManta ray foraging optimizer-based image segmentation with a two-strategy enhancement-
dc.typeArticle-
dc.identifier.doi10.1016/j.knosys.2022.110247-
dc.identifier.scopuseid_2-s2.0-85147114883-
dc.identifier.volume262-
dc.identifier.eissn1872-7409-
dc.identifier.isiWOS:000925253000001-
dc.publisher.placeAMSTERDAM-
dc.identifier.issnl0950-7051-

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