By Topic

An Efficient Dynamic Image Segmentation Algorithm Using a Hybrid Technique Based on Particle Swarm Optimization and Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kole, D.K. ; Dept. of Comput. Sc. & Eng., St. Thomas'' Coll. of Eng. & Tech., Kolkata, India ; Halder, A.

This paper describe a new approach to automatic unsupervised efficient image segmentation algorithm using hybrid technique based on Particle Swarm Optimization and Genetic Algorithm. This technique uses the PSO based dynamic clustering approach to predict the optimal number clusters which is required to partition the data set. This prediction is then used by the GA based module to improve the final result (global best particle) of the PSO based method. The best number of clusters is obtained by using cluster validity criterion with the help of Gaussian distribution. The proposed algorithm is evaluated on well known natural images and its performance is compared to that of DCPSO, snob and SOM based clustering techniques. Experimental results demonstrate the performance of the proposed algorithm producing comparable segmentation results.

Published in:

Advances in Computer Engineering (ACE), 2010 International Conference on

Date of Conference:

20-21 June 2010