A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding
Joint Authors
Chiewchanwattana, Sirapat
Sunat, Khamron
Charansiriphaisan, Kanjana
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-28
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Multilevel thresholding is a highly useful tool for the application of image segmentation.
Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost.
There has been a lot of recent research into various meta-heuristic searches in the area of optimization research.
This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding.
The strategy for an onlooker bee to select an employee bee was modified to serve our purposes.
The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance.
The ranking was performed by Friedman ranks.
The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding.
Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.
American Psychological Association (APA)
Charansiriphaisan, Kanjana& Chiewchanwattana, Sirapat& Sunat, Khamron. 2013. A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-1011188
Modern Language Association (MLA)
Charansiriphaisan, Kanjana…[et al.]. A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding. Mathematical Problems in Engineering No. 2013 (2013), pp.1-17.
https://search.emarefa.net/detail/BIM-1011188
American Medical Association (AMA)
Charansiriphaisan, Kanjana& Chiewchanwattana, Sirapat& Sunat, Khamron. A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-17.
https://search.emarefa.net/detail/BIM-1011188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1011188