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dc.contributor.authorAteş, Hasan Fehmien_US
dc.contributor.authorSünetci, Sercanen_US
dc.date.accessioned2019-03-26T23:17:55Z
dc.date.available2019-03-26T23:17:55Z
dc.date.issued2017
dc.identifier.citationAteş, H. F. & Sünetci, S. (2017). Improving semantic segmentation with generalized models of local context. Paper presented at the 17th International Conference on Computer Analysis of Images and Patterns (CAIP), 10425 320-330. doi:10.1007/978-3-319-64698-5_27en_US
dc.identifier.isbn9783319646978
dc.identifier.isbn9783319646985
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.otherWOS:000432084600027
dc.identifier.urihttps://hdl.handle.net/11729/1507
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-64698-5_27
dc.description.abstractSemantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Superpixel image parsing methods provide this consistency by carrying out labeling at the superpixel-level based on superpixel features and neighborhood information. In this paper, we develop generalized and flexible contextual models for superpixel neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models to combine complementary information available in alternative superpixel segmentations of the same image. Simulation results on two datasets demonstrate significant improvement in parsing accuracy over the baseline approach.en_US
dc.description.sponsorshipThis work is supported in part by TUBITAK project no: 115E307 and by Isik University BAP project no: 14A205en_US
dc.language.isoengen_US
dc.publisherSpringer International Publishing AGen_US
dc.relation.isversionof10.1007/978-3-319-64698-5_27
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectImage parsingen_US
dc.subjectSegmentationen_US
dc.subjectSuperpixelen_US
dc.subjectMRFen_US
dc.titleImproving semantic segmentation with generalized models of local contexten_US
dc.typeconferenceObjecten_US
dc.description.versionPublisher's Versionen_US
dc.relation.journal17th International Conference on Computer Analysis of Images and Patterns (CAIP)en_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.contributor.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.contributor.authorID0000-0002-6842-1528
dc.identifier.volume10425
dc.identifier.startpage320
dc.identifier.endpage330
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorAteş, Hasan Fehmien_US
dc.contributor.institutionauthorSünetci, Sercanen_US


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