Next Article in Journal
Entropy Harvesting
Previous Article in Journal
Thermoelectric System in Different Thermal and Electrical Configurations: Its Impact in the Figure of Merit
Entropy 2013, 15(6), 2181-2209; doi:10.3390/e15062181
Article

An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms

*  and
Received: 1 March 2013 / Revised: 3 May 2013 / Accepted: 23 May 2013 / Published: 3 June 2013
Download PDF [1974 KB, uploaded 24 February 2015]

Abstract

Multilevel thresholding has been long considered as one of the most popular techniques for image segmentation. Multilevel thresholding outputs a gray scale image in which more details from the original picture can be kept, while binary thresholding can only analyze the image in two colors, usually black and white. However, two major existing problems with the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take an exceptionally long computation time; and defining a proper number of thresholds or levels that will keep most of the relevant details from the original image is a difficult task. In this study a new evaluation function based on the Kullback-Leibler information distance, also known as relative entropy, is proposed. The property of this new function can help determine the number of thresholds automatically. To offset the expensive computational effort by traditional exhaustive search methods, this study establishes a procedure that combines the relative entropy and meta-heuristics. From the experiments performed in this study, the proposed procedure not only provides good segmentation results when compared with a well known technique such as Otsu’s method, but also constitutes a very efficient approach.
Keywords: thresholding; relative entropy; Gaussian mixture models; meta-heuristics; virus optimization algorithm thresholding; relative entropy; Gaussian mixture models; meta-heuristics; virus optimization algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Liang, Y.-C.; Cuevas, J.R. An Automatic Multilevel Image Thresholding Using Relative Entropy and Meta-Heuristic Algorithms. Entropy 2013, 15, 2181-2209.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert