Saliency detection based on hybrid artificial bee colony and firefly optimization

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dc.authorid0000-0002-3645-3445
dc.authorid0000-0001-7489-9053
dc.authorid0000-0003-3820-6453
dc.contributor.authorYelmenoğlu, Elif Denizen_US
dc.contributor.authorÇelebi, Numanen_US
dc.contributor.authorTaşçı, Tuğrulen_US
dc.date.accessioned2022-05-20T12:46:42Z
dc.date.available2022-05-20T12:46:42Z
dc.date.issued2022-11
dc.departmentIşık Üniversitesi, Fen Edebiyat Fakültesi, Enformasyon Teknolojileri Bölümüen_US
dc.departmentIşık University, Faculty of Arts and Sciences, Department of Information Technologiesen_US
dc.description.abstractSaliency detection is one of the challenging problems still tackled by image processing and computer vision research communities. Although not very numerous, recent studies reveal that optimization-based methods provide relatively accurate and fast solutions for such problems. This paper presents a novel unsupervised hybrid optimization method that aims to propose reasonable solution to saliency detection problem by combining the familiar artificial bee colony and firefly algorithms. The proposed method, HABCFA, is based on creating hybrid-personality individuals behaving like both bees and fireflies. A superpixel-based method is used to obtain better background intensity values in the saliency detection process, providing a better precision in extracting the salient regions. HABCFA algorithm is capable of achieving an optimum saliency map without requiring any extra mask or training step. HABCFA has produced superior performance against its basis algorithms, artificial bee colony, and firefly on four known benchmark problems regarding convergence rate and iteration count. On the other hand, the experimental results on four commonly used datasets, including MSRA-1000, ECSSD, ICOSEG, and DUTOMRON, demonstrate that HABCFA is adequately robust and effective in terms of accuracy, precision, and speed in comparison with the eleven state-of-the-art methods.en_US
dc.identifier.citationYelmenoğlu, E. D., Çelebi, N. & Taşçı, T. (2022). Saliency detection based on hybrid artificial bee colony and firefly optimization. Pattern Analysis and Applications, 25(4), 757-772. doi:10.1007/s10044-022-01063-6en_US
dc.identifier.doi10.1007/s10044-022-01063-6
dc.identifier.endpage772
dc.identifier.issn1433-7541
dc.identifier.issn1433-755X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85127634374
dc.identifier.scopusqualityQ2
dc.identifier.startpage757
dc.identifier.urihttps://hdl.handle.net/11729/4343
dc.identifier.urihttp://dx.doi.org/10.1007/s10044-022-01063-6
dc.identifier.volume25
dc.identifier.wosWOS:000781280300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorYelmenoğlu, Elif Denizen_US
dc.institutionauthorid0000-0002-3645-3445
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofPattern Analysis and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial bee colonyen_US
dc.subjectFireflyen_US
dc.subjectOptimizationen_US
dc.subjectSaliency detectionen_US
dc.subjectSuperpixelen_US
dc.subjectSaliencyen_US
dc.subjectObject detectionen_US
dc.subjectVisual attentionen_US
dc.subjectRecognitionen_US
dc.subjectAttentionen_US
dc.titleSaliency detection based on hybrid artificial bee colony and firefly optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication

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