Bio-inspired algorithms for multilevel image thresholding Online publication date: Mon, 02-Mar-2015
by Salima Ouadfel; Souham Meshoul
International Journal of Computer Applications in Technology (IJCAT), Vol. 49, No. 3/4, 2014
Abstract: Bi-level image thresholding methods can be easily extended to multilevel cases. However, extended versions are computationally expensive. In this paper, we propose first a differential evolution (DE) algorithm using Tsallis entropy as objective function. Second, we conduct a comprehensive comparative study by investigating the potential of the proposed algorithm to find the optimal threshold values along with two other bio-inspired algorithms namely artificial bees colony (ABC) and particle swarm optimisation (PSO). Two entropy-based measures have been considered as objective functions. Real images with different complexities have been used to evaluate the performance of the three algorithms. Experimental results demonstrated that DE and ABC achieve the same quality of solutions in terms of peak signal to noise ratio values and uniformity values. They are more robust than PSO. Furthermore, DE has shown to be the most stable and ABC the fastest with the advantage of employing few control parameters.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Computer Applications in Technology (IJCAT):
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook