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Cluster analysis and optimization in color-based clustering for image abstract

He, Jing, Huang, Guangyan, Zhang, Yanchun and Shi, Yong 2007, Cluster analysis and optimization in color-based clustering for image abstract, in ICDMW 2007: Proceedings of the 7th IEEE International Conference on Data Mining, IEEE, Piscataway, N.J., pp. 213-218, doi: 10.1109/ICDMW.2007.41.

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Title Cluster analysis and optimization in color-based clustering for image abstract
Author(s) He, Jing
Huang, Guangyan
Zhang, Yanchun
Shi, Yong
Conference name IEEE International Conference on Data Mining Workshops (7th : 2007 : Omaha, Nebraska)
Conference location Omaha, Nebraska
Conference dates 28-31 Oct. 2007
Title of proceedings ICDMW 2007: Proceedings of the 7th IEEE International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2007
Conference series IEEE International Conference on Data Mining
Start page 213
End page 218
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Cluster analysis has been identified as a core task in data mining. What constitutes a cluster, or a good clustering, may depend on the background of researchers and applications. This paper proposes two optimization criteria of abstract degree and fidelity in the field of image abstract. To satisfy the fidelity criteria, a novel clustering algorithm named Global Optimized Color-based DBSCAN Clustering (GOC-DBSCAN) is provided. Also, non-optimized local color information based version of GOC-DBSCAN, called HSV-DBSCAN, is given. Both of them are based on HSV color space. Clusters of GOC-DBSCAN are analyzed to find the factors that impact on the performance of both abstract degree and fidelity. Examples show generally the greater the abstract degree is, the less is the fidelity. It also shows GOC-DBSCAN outperforms HSV-DBSCAN when they are evaluated by the two optimization criteria.
ISBN 0769530192
9780769530192
ISSN 1550-4786
Language eng
DOI 10.1109/ICDMW.2007.41
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083684

Document type: Conference Paper
Collection: School of Information Technology
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