dc.contributor.author | Ateş, Hasan Fehmi | en_US |
dc.contributor.author | Sünetci, Sercan | en_US |
dc.date.accessioned | 2019-03-26T23:17:55Z | |
dc.date.available | 2019-03-26T23:17:55Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Ateş, 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_27 | en_US |
dc.identifier.isbn | 9783319646978 | |
dc.identifier.isbn | 9783319646985 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | https://hdl.handle.net/11729/1507 | |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-64698-5_27 | |
dc.description.abstract | Semantic 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.sponsorship | This work is supported in part by TUBITAK project no: 115E307 and by Isik University BAP project no: 14A205 | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer International Publishing AG | en_US |
dc.relation.isversionof | 10.1007/978-3-319-64698-5_27 | |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Image parsing | en_US |
dc.subject | Segmentation | en_US |
dc.subject | Superpixel | en_US |
dc.subject | MRF | en_US |
dc.title | Improving semantic segmentation with generalized models of local context | en_US |
dc.type | conferenceObject | en_US |
dc.description.version | Publisher's Version | en_US |
dc.relation.journal | 17th International Conference on Computer Analysis of Images and Patterns (CAIP) | en_US |
dc.contributor.department | Işık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.contributor.department | Işık University, Faculty of Engineering, Department of Electrical-Electronics Engineering | en_US |
dc.contributor.authorID | 0000-0002-6842-1528 | |
dc.identifier.volume | 10425 | |
dc.identifier.startpage | 320 | |
dc.identifier.endpage | 330 | |
dc.peerreviewed | Yes | en_US |
dc.publicationstatus | Published | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.contributor.institutionauthor | Ateş, Hasan Fehmi | en_US |
dc.contributor.institutionauthor | Sünetci, Sercan | en_US |
dc.relation.index | WOS | en_US |
dc.relation.index | Scopus | en_US |
dc.relation.index | Conference Proceedings Citation Index – Science (CPCI-S) | en_US |
dc.description.quality | Q4 | |
dc.description.wosid | WOS:000432084600027 | |