Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6378
Title: Class Representative Computation using Graph Embedding and Clustering
Authors: Aydos, Fahri
Demirci, Muhammed Fatih
Keywords: Object Recognition
Graph Embedding
Clustering
Issue Date: 2013
Publisher: IEEE
Source: 21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: One of the methods for object recognition is based on graph embedding. By representing objects expressed as graphs into the vector space, this technique makes it possible to use point matching algorithms as opposed to costly graph matching approaches. In this paper, representatives of object classes in the vector space is obtained through graph embedding. To classify a query, instead of using exhaustive search, a more effective way of comparing it to class representatives is employed. Experimental results demonstrate that the proposed work compares favorably to alternative approaches in a set of object recognition experiments.
URI: https://hdl.handle.net/20.500.11851/6378
ISBN: 978-1-4673-5563-6; 978-1-4673-5562-9
ISSN: 2165-0608
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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