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https://hdl.handle.net/20.500.11851/6377
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aydos, Fahri | - |
dc.contributor.author | Soran, Ahmet | - |
dc.contributor.author | Demirci, Muhammed Fatih | - |
dc.date.accessioned | 2021-09-11T15:36:08Z | - |
dc.date.available | 2021-09-11T15:36:08Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | 17th International Conference on Image Analysis and Processing (ICIAP) -- SEP 09-13, 2013 -- Naples, ITALY | en_US |
dc.identifier.isbn | 978-3-642-41181-6; 978-3-642-41180-9 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6377 | - |
dc.description.abstract | Due 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.sponsorship | Univ 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 srl | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer-Verlag Berlin | en_US |
dc.relation.ispartof | Image Analysis And Processing (Iciap 2013), Pt 1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | object recognition | en_US |
dc.subject | graph embedding | en_US |
dc.subject | clustering | en_US |
dc.title | Class Representative Computation Using Graph Embedding | en_US |
dc.type | Conference Object | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science | 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 | 8156 | en_US |
dc.identifier.startpage | 452 | en_US |
dc.identifier.endpage | 461 | en_US |
dc.identifier.wos | WOS:000329804300046 | en_US |
dc.identifier.scopus | 2-s2.0-84884718320 | en_US |
dc.institutionauthor | Demirci, Muhammed Fatih | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 17th International Conference on Image Analysis and Processing (ICIAP) | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Conference Object | - |
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 | 02.3. 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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