Saliency detection based on hybrid artificial bee colony and firefly optimization
dc.authorid | 0000-0002-3645-3445 | |
dc.authorid | 0000-0002-3645-3445 | en_US |
dc.contributor.author | Yelmenoğlu, Elif Deniz | en_US |
dc.contributor.author | Çelebi, Numan | en_US |
dc.contributor.author | Taşçı, Tuğrul | en_US |
dc.date.accessioned | 2022-05-20T12:46:42Z | |
dc.date.available | 2022-05-20T12:46:42Z | |
dc.date.issued | 2022-11 | |
dc.department | Işık Üniversitesi, Fen Edebiyat Fakültesi, Enformasyon Teknolojileri Bölümü | en_US |
dc.department | Işık University, Faculty of Arts and Sciences, Department of Information Technologies | en_US |
dc.description.abstract | Saliency 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.citation | Yelmenoğlu, 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/s10044-022-01063-6 | en_US |
dc.identifier.doi | 10.1007/s10044-022-01063-6 | |
dc.identifier.endpage | 772 | |
dc.identifier.issn | 1433-7541 | en_US |
dc.identifier.issn | 1433-755X | en_US |
dc.identifier.issue | 4 | |
dc.identifier.scopus | 2-s2.0-85127634374 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.startpage | 757 | |
dc.identifier.uri | https://hdl.handle.net/11729/4343 | |
dc.identifier.uri | http://dx.doi.org/10.1007/s10044-022-01063-6 | |
dc.identifier.volume | 25 | |
dc.identifier.wos | WOS:000781280300001 | en_US |
dc.identifier.wosquality | Q2 | |
dc.identifier.wosquality | Q2 | en_US |
dc.indekslendigikaynak | Science Citation Index Expanded (SCI-EXPANDED) | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.indekslendigikaynak | Science Citation Index Expanded (SCI-EXPANDED) | en_US |
dc.institutionauthor | Yelmenoğlu, Elif Deniz | en_US |
dc.language.iso | en | en_US |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Pattern Analysis and Applications | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial bee colony | en_US |
dc.subject | Firefly | en_US |
dc.subject | Optimization | en_US |
dc.subject | Saliency detection | en_US |
dc.subject | Superpixel | en_US |
dc.subject | Saliency | en_US |
dc.subject | Object detection | en_US |
dc.subject | Visual attention | en_US |
dc.subject | Recognition | en_US |
dc.subject | Attention | en_US |
dc.title | Saliency detection based on hybrid artificial bee colony and firefly optimization | en_US |
dc.type | Article | en_US |
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