By Topic

Improving Image Segmentation Using 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
$33 $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

3 Author(s)
Huynh Thi Thanh Binh ; Sch. of Inf. & Commun. Technol., Hanoi Univ. of Sci. & Technol., Hanoi, Vietnam ; Mai Dinh Loi ; Nguyen Thi Thuy

This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature [28], which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We conducted extensive experiments on benchmark image datasets and compared the segmentation results with current proposed systems. The experimental results show that the performance of our system is comparable to, or even outperforms, those state-of-the-art algorithms. This is a promising approach as in this empirical study we used only texture-layout filter responses as feature and a basic setting of genetic algorithm. The framework is simple and can be extended and improved for many learning problems.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:2 )

Date of Conference:

12-15 Dec. 2012