An empirical analysis of colour image segmentation using fuzzy c-means clustering

Lim, C. P. and Ooi, W. S. 2010, An empirical analysis of colour image segmentation using fuzzy c-means clustering, International journal of knowledge engineering and soft data paradigms, vol. 2, no. 1, pp. 97-106.

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Title An empirical analysis of colour image segmentation using fuzzy c-means clustering
Author(s) Lim, C. P.
Ooi, W. S.
Journal name International journal of knowledge engineering and soft data paradigms
Volume number 2
Issue number 1
Start page 97
End page 106
Total pages 10
Publisher Inderscience Publishers
Place of publication Geneva, Switzerland
Publication date 2010
ISSN 1755-3210
1755-3229
Keyword(s) image segmentation
fuzzy c-means clustering
colour models
pixel clustering
CIELAB
Summary In this paper, an empirical analysis to examine the effects of image segmentation with different colour models using the fuzzy c-means (FCM) clustering algorithm is conducted. A qualitative evaluation method based on human perceptual judgement is used. Two sets of complex images, i.e., outdoor scenes and satellite imagery, are used for demonstration. These images are employed to examine the characteristics of image segmentation using FCM with eight different colour models. The results obtained from the experimental study are compared and analysed. It is found that the CIELAB colour model yields the best outcomes in colour image segmentation with FCM.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Inderscience Enterprises Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048114

Document type: Journal Article
Collection: Institute for Frontier Materials
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