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Clustering based multi-label classification for image annotation and retrieval

Nasierding, Gulisong, Tsoumakas, Grigorios and Kouzani, Abbas Z. 2009, Clustering based multi-label classification for image annotation and retrieval, in SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N. J., pp. 4514-4519.

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Title Clustering based multi-label classification for image annotation and retrieval
Author(s) Nasierding, Gulisong
Tsoumakas, Grigorios
Kouzani, Abbas Z.
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2009 : San Antonio, Texas)
Conference location San Antonio, Texas
Conference dates 11-14 Oct. 2009
Title of proceedings SMC 2009 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2009
Conference series International Conference on Systems, Man and Cybernetics
Start page 4514
End page 4519
Total pages 6
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) clustering
multi-label classification
automatic image annotation
Summary This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.
ISBN 9781424427932
ISSN 1062-922X
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890402 Film and Video Services (excl. Animation and Computer Generated Imagery)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029209

Document type: Conference Paper
Collections: School of Engineering
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.