Sequential image segmentation based on minimum spanning tree representation

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dc.contributor.authorSaglam, Ali
dc.contributor.authorBaykan, Nurdan Akhan
dc.date.accessioned2020-03-26T19:42:36Z
dc.date.available2020-03-26T19:42:36Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description10th IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition (GbR) -- MAY 13-15, 2015 -- Beijing, PEOPLES R CHINAen_US
dc.description.abstractImage segmentation is a very important stage in various image processing applications. Segmentation of pixels of an image and clustering of data are closely related to each other. For many graph-based data-clustering methods and many graph-based image-segmentation methods, minimum spanning tree (MST)-based approaches play a crucial role because of their ease of operation and low computational complexity. In this paper, we improve a successful data-clustering algorithm that uses Prim's sequential representation of MST, for the purpose of image segmentation. The algorithm runs by scanning the complete MST structure of the entire image, such that it finds, and then cuts, inconsistent edges among a constantly changing juxtaposed edge string whose elements are obtained from the MST at a specific length. In our method, the length of the string not only determines the edges to compare, but also helps to remove the small, undesired cluster particles. We also develop a new predicate for the cutting criterion. The criterion takes into account several local and global features that differ from image to image. We test our algorithm on a database that consists of real images. The results show that the proposed method can compete with the most popular image segmentation algorithms in terms of low execution time. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipIAPR TC 15 Graph Based Representat Pattern Recogniten_US
dc.description.sponsorshipSelcuk University OYP Coordination and Scientific Research Project of Selcuk Universityen_US
dc.description.sponsorshipThis study was supported by Selcuk University OYP Coordination and Scientific Research Project of Selcuk University.en_US
dc.identifier.doi10.1016/j.patrec.2016.06.001en_US
dc.identifier.endpage162en_US
dc.identifier.issn0167-8655en_US
dc.identifier.issn1872-7344en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage155en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.patrec.2016.06.001
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35467
dc.identifier.volume87en_US
dc.identifier.wosWOS:000395616700019en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE BVen_US
dc.relation.ispartofPATTERN RECOGNITION LETTERSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSegmentationen_US
dc.subjectClusteringen_US
dc.subjectGraphen_US
dc.subjectMinimum spanning treeen_US
dc.subjectPrimen_US
dc.titleSequential image segmentation based on minimum spanning tree representationen_US
dc.typeArticleen_US

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