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, doi: 10.1109/ICSMC.2009.5346902. Attached Files Name Description MIMEType Size Downloads kouzani-clusteringbased-2009.pdf Published version application/pdf 168.69KB 4829 Title Clustering based multi-label classification for image annotation and retrieval Author(s) Nasierding, Gulisong Tsoumakas, Grigorios Kouzani, Abbas Z. orcid.org/0000-0002-6292-1214 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 DOI 10.1109/ICSMC.2009.5346902 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30029209 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Thu, 10 Jun 2010, 12:30:56 EST Thu, 10 Jun 2010, 12:36:23 EST Thu, 10 Jun 2010, 12:37:22 EST Mon, 20 Feb 2012, 13:01:59 EST Tue, 21 Feb 2012, 07:22:08 EST Fri, 16 Mar 2012, 18:07:18 EST Mon, 23 Sep 2013, 11:54:43 EST Fri, 06 Feb 2015, 10:36:45 EST Tue, 06 Sep 2016, 15:21:39 EST Tue, 05 Dec 2017, 09:16:09 EST Fri, 07 Sep 2018, 14:59:21 EST Filtered Full Citation counts:   Cited 51 times in TR Web of Science Cited 68 times in Scopus Search Google Scholar Access Statistics: 1177 Abstract Views, 4830 File Downloads  -  Detailed Statistics Created: Thu, 10 Jun 2010, 12:30:55 EST by Leanne Swaneveld