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The reduction subset based on rough sets applied to texture classification

Pan, Li, Nahavandi, Saeid and Lan, Jinhui 2003, The reduction subset based on rough sets applied to texture classification, in Program & abstracts : the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems : CIRAS 2003 : 15-18 December 2003, Pan Pacific Hotel, Singapore, Center for Intelligent Control, National University of Singapore, Singapore.

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Title The reduction subset based on rough sets applied to texture classification
Author(s) Pan, Li
Nahavandi, Saeid
Lan, Jinhui
Conference name International Conference on Computational Intelligence, Robotics and Autonomous Systems (2nd : 2003 : Singapore)
Conference location Singapore
Conference dates 15-18 December 2003
Title of proceedings Program & abstracts : the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems : CIRAS 2003 : 15-18 December 2003, Pan Pacific Hotel, Singapore
Editor(s) Vadakkepat, Prahlad
Publication date 2003
Publisher Center for Intelligent Control, National University of Singapore
Place of publication Singapore
Summary The rough set is a new mathematical approach to imprecision, vagueness and uncertainty. The concept of reduction of the decision table based on the rough sets is very useful for feature selection. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The methods applied include continuous data discretization based on Fuzzy c-means and, and rough set method for feature selection and reduction. The trees extractions in the aerial images were applied. The experiments show that the methods presented in this paper are practical and effective.
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ISSN 0219-6131
Language eng
Field of Research 099999 Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003, CIRAS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005249

Document type: Conference Paper
Collections: School of Engineering and Technology
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