Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/1176
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aksaç, Alper | - |
dc.contributor.author | Özyer, Tansel | - |
dc.contributor.author | Alhajj, Reda | - |
dc.date.accessioned | 2019-06-26T07:40:36Z | |
dc.date.available | 2019-06-26T07:40:36Z | |
dc.date.issued | 2017-06 | |
dc.identifier.citation | Aksac, A., Ozyer, T., & Alhajj, R. (2017). Complex networks driven salient region detection based on superpixel segmentation. Pattern Recognition, 66, 268-279. | en_US |
dc.identifier.issn | 0031-3203 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0031320317300110?via%3Dihub | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/1176 | - |
dc.description.abstract | In this paper, we propose an efficient method for salient region detection. First, the image is decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. Based upon the generated superpixels, similarity between the regions is calculated by benefiting from color, location, histogram, intensity, and area information of each region as well as community identification via complex networks theory in the over-segmented image. Then, contrast, distribution and complex networks based saliency maps are generated by using the mentioned features. These saliency maps are used to create a final saliency map. The applicability, effectiveness and consistency of the proposed approach are illustrated by conducting some experiments using publicly available datasets. The tests have been used to compare the proposed method with some state-of-the-art methods. The reported results cover qualitative and quantitative assessments which demonstrate that our approach outputs high quality saliency maps and mostly achieves the highest precision rate compared to the other methods. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science Ltd. | en_US |
dc.relation.ispartof | Pattern Recognition | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Superpixel | en_US |
dc.subject | Segmentation | en_US |
dc.subject | Salient Region Detection | en_US |
dc.subject | Saliency Map | en_US |
dc.subject | Complex Networks | en_US |
dc.title | Complex Networks Driven Salient Region Detection Based on Superpixel Segmentation | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 66 | |
dc.identifier.startpage | 268 | |
dc.identifier.endpage | 279 | |
dc.identifier.wos | WOS:000397371800025 | en_US |
dc.identifier.scopus | 2-s2.0-85011039345 | en_US |
dc.institutionauthor | Özyer, Tansel | - |
dc.identifier.doi | 10.1016/j.patcog.2017.01.010 | - |
dc.authorscopusid | 8914139000 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | - | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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