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Empirical study of multi-label classification methods for image annotation and retrieval

Nasierding, Gulisong and Kouzani, Abbas Z. 2010, Empirical study of multi-label classification methods for image annotation and retrieval, in DICTA 2010 : Proceedings of the Digital Image Computing : Techniques and Application, DICTA, Sydney, NSW, pp. 617-622.

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Title Empirical study of multi-label classification methods for image annotation and retrieval
Author(s) Nasierding, Gulisong
Kouzani, Abbas Z.
Conference name Digital Image Computing : Techniques and Application Conference (2010 : Sydney, N.S.W.)
Conference location Sydney, NSW
Conference dates 1-3 Dec. 2010
Title of proceedings DICTA 2010 : Proceedings of the Digital Image Computing : Techniques and Application
Editor(s) [Unknown]
Publication date 2010
Conference series Australian Pattern Recognition Society Conference
Start page 617
End page 622
Total pages 6
Publisher DICTA
Place of publication Sydney, NSW
Keyword(s) multi-label classification
image annotation and retrieval
empirical study
Summary This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.
ISBN 9781424488162
Language eng
Field of Research 090609 Signal Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032344

Document type: Conference Paper
Collections: School of Engineering
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Created: Mon, 24 Jan 2011, 23:38:38 EST by Abbas Kouzani

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.