NDLI: Multilevel image thresholding using relative entropy and Virus Optimization Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yun-Chia Liang Cuevas J, J.R.
Copyright Year	2012
Description	Author affiliation: Department of Industrial Engineering and Management, Yuan Ze University, Taiwan (Yun-Chia Liang; Cuevas J, J.R.)
Abstract	One of the most popular techniques for image segmentation is known as multilevel thresholding. The main difference between multilevel and binary thresholding, is that the binary thresholding outputs a two-color image, usually black and white, while the multilevel thresholding outputs a grey scale image in which more details from the original picture can be kept. Two major problems with using the multilevel thresholding technique are: it is a time consuming approach, i.e., finding appropriate threshold values could take exceptionally long computational time; 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 approach based on the Kullback-Leibler information distance, also known as Relative Entropy, is proposed. The approach minimizes a mathematical model, which will determine the number of thresholds automatically. The optimization of the mathematical model is achieved by using a newly developed meta-heuristic named Virus Optimization Algorithm (VOA), where its performance is compared with Genetic Algorithm (GA). From the experiments performed in this study, the proposed method does not only provide good segmentation results but also its computational effort makes it a very efficient approach.
Starting Page	1
Ending Page	8
File Size	1212235
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Viruses (medical) Mathematical model Genetic algorithms Optimization Image segmentation Entropy Biological cells virus optimization algorithm thresholding relative entropy Gaussian mixture models
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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