Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6377
Title: Class Representative Computation Using Graph Embedding
Authors: Aydos, Fahri
Soran, Ahmet
Demirci, Muhammed Fatih
Keywords: object recognition
graph embedding
clustering
Issue Date: 2013
Publisher: Springer-Verlag Berlin
Source: 17th International Conference on Image Analysis and Processing (ICIAP) -- SEP 09-13, 2013 -- Naples, ITALY
Series/Report no.: Lecture Notes in Computer Science
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.
URI: https://hdl.handle.net/20.500.11851/6377
ISBN: 978-3-642-41181-6; 978-3-642-41180-9
ISSN: 0302-9743
1611-3349
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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