Matching of matching-graphs - a novel approach for graph classification
Loading...
Authors
Author (Corporation)
Publication date
2020
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
2020 25th International Conference on Pattern Recognition (ICPR)
Special issue
DOI of the original publication
Link
Series
Series number
Volume
Issue / Number
Pages / Duration
6570-6576
Patent number
Publisher / Publishing institution
IEEE
Place of publication / Event location
Milano
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Due to fast developments in data acquisition, we observe rapidly increasing amounts of data available in diverse areas. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs a very useful and adequate data structure for formal representation. A large amount of graph based methods for pattern recognition have been proposed. Many of these methods actually rely on graph matching. In the present paper a novel encoding of graph matching information is proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs. In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification.
Keywords
Subject (DDC)
Event
International Conference on Pattern Recognition (ICPR) 2020
Exhibition start date
Exhibition end date
Conference start date
10.01.2021
Conference end date
15.01.2021
Date of the last check
ISBN
978-1-7281-8808-9
978-1-7281-8809-6
978-1-7281-8809-6
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Fuchs, M., & Riesen, K. (2020). Matching of matching-graphs - a novel approach for graph classification. 2020 25th International Conference on Pattern Recognition (ICPR), 6570–6576. https://doi.org/10.1109/ICPR48806.2021.9411926