Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6377
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dc.contributor.authorAydos, Fahri-
dc.contributor.authorSoran, Ahmet-
dc.contributor.authorDemirci, Muhammed Fatih-
dc.date.accessioned2021-09-11T15:36:08Z-
dc.date.available2021-09-11T15:36:08Z-
dc.date.issued2013en_US
dc.identifier.citation17th International Conference on Image Analysis and Processing (ICIAP) -- SEP 09-13, 2013 -- Naples, ITALYen_US
dc.identifier.isbn978-3-642-41181-6; 978-3-642-41180-9-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6377-
dc.description.abstractDue to representative power of graphs, graph-based object recognition has received a great deal of research attention in literature. Given an object represented as a graph, performing graph matching with each member of the database in order to locate the graph which most resembles the query is inefficient especially when the size of the database is large. In this paper we propose an algorithm which represents the graphs belonging to a particular set as points through graph embedding and operates in the vector space to compute the representative of the set. We use the k-means clustering algorithm to learn centroids forming the representatives. Once the representative of each set is obtained, we embed the query into the vector space and compute the matching in this space. The query is classified into the most similar representative of a set. This way, we are able to overcome the complexity of graph matching and still perform the classification for the query effectively. Experimental evaluation of the proposed work demonstrates the efficiency, effectiveness, and stability of the overall approach.en_US
dc.description.sponsorshipUniv Naples Parthenope, CVPR Lab, Campania Reg Board, Natl Res Council Italy, Italian Minist Educ, Univ & Res, Italian Minist Econ Dev, Comune Napoli, Google Inc, AnsaldoSTS, Italian Aerosp Res Ctr, Selex ES, ST Microelectron, Unlimited Software srlen_US
dc.language.isoenen_US
dc.publisherSpringer-Verlag Berlinen_US
dc.relation.ispartofImage Analysis And Processing (Iciap 2013), Pt 1en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectobject recognitionen_US
dc.subjectgraph embeddingen_US
dc.subjectclusteringen_US
dc.titleClass Representative Computation Using Graph Embeddingen_US
dc.typeConference Objecten_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume8156en_US
dc.identifier.startpage452en_US
dc.identifier.endpage461en_US
dc.identifier.wosWOS:000329804300046en_US
dc.identifier.scopus2-s2.0-84884718320en_US
dc.institutionauthorDemirci, Muhammed Fatih-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference17th International Conference on Image Analysis and Processing (ICIAP)en_US
dc.identifier.scopusqualityQ2-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.3. Department of Computer 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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