DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sun Jeong | ko |
dc.contributor.author | Lee, Han Sang | ko |
dc.contributor.author | Kim, Junmo | ko |
dc.date.accessioned | 2017-03-31T05:41:20Z | - |
dc.date.available | 2017-03-31T05:41:20Z | - |
dc.date.created | 2016-11-21 | - |
dc.date.created | 2016-11-21 | - |
dc.date.issued | 2017-01 | - |
dc.identifier.citation | ELECTRONICS LETTERS, v.53, no.1, pp.22 - 23 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/222784 | - |
dc.description.abstract | In an interactive image segmentation, the quantity of a user-given seed is known to affect the segmentation accuracy. In this Letter, we propose a seed-growing method expanding the quantity of a seed to reduce the bias of the given seed and improve the segmentation accuracy. To grow the given seed, a supervised classification framework with geodesic distance features is proposed. From a single input image, a support vector machine (SVM) classifier is trained on the seed superpixels of an input image. Other non-seed superpixels are then classified into object, background and non-seed regions by the trained classifier. In experiments, the proposed method showed promising results by improving the segmentation accuracy of existing segmentation methods in public benchmark datasets. | - |
dc.language | English | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.subject | GRAPH CUTS | - |
dc.title | Seed growing for interactive image segmentation using SVM classification with geodesic distance | - |
dc.type | Article | - |
dc.identifier.wosid | 000393750600012 | - |
dc.identifier.scopusid | 2-s2.0-85007320379 | - |
dc.type.rims | ART | - |
dc.citation.volume | 53 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 22 | - |
dc.citation.endingpage | 23 | - |
dc.citation.publicationname | ELECTRONICS LETTERS | - |
dc.identifier.doi | 10.1049/el.2016.3919 | - |
dc.contributor.localauthor | Kim, Junmo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | image segmentation | - |
dc.subject.keywordAuthor | support vector machines | - |
dc.subject.keywordAuthor | image classification | - |
dc.subject.keywordAuthor | differential geometry | - |
dc.subject.keywordAuthor | learning (artificial intelligence) | - |
dc.subject.keywordAuthor | interactive image segmentation | - |
dc.subject.keywordAuthor | SVM classification | - |
dc.subject.keywordAuthor | geodesic distance | - |
dc.subject.keywordAuthor | seed-growing method | - |
dc.subject.keywordAuthor | supervised classification framework | - |
dc.subject.keywordAuthor | support vector machine | - |
dc.subject.keywordAuthor | training | - |
dc.subject.keywordAuthor | nonseed superpixel classification | - |
dc.subject.keywordAuthor | public benchmark dataset | - |
dc.subject.keywordPlus | GRAPH CUTS | - |
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