An empirical analysis of colour image segmentation using fuzzy c-means clustering Online publication date: Thu, 17-Dec-2009
by C.P. Lim, W.S. Ooi
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 2, No. 1, 2010
Abstract: 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.
Online publication date: Thu, 17-Dec-2009
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com