A genetic algorithm-based clustering and two-scan labelling for colour image segmentation
by F.Z. Bellala; Feryel Souami
International Journal of Computational Vision and Robotics (IJCVR), Vol. 4, No. 1/2, 2014

Abstract: In this paper, we present a two-step segmentation method. The first consists of colour image quantisation by genetic algorithm-based clustering method. The second consists of connected component labelling (CCL) of quantised image. In the first step, we use real codification of chromosomes and a variable string length to adjust the number of colours of the reduced palette. We use a fitness function with a smallest number of parameters to improve run time. Once pixels are classified in 3D colour space, a two-scan CCL adapted to colour image is proposed and applied to the 2D image plan to get separate regions. To reduce regions number, small regions are grouped with the nearest surrounding regions according to their colour feature. Segmentation results are discussed in RGB and lab colour spaces.

Online publication date: Sat, 21-Jun-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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 subs@inderscience.com