Image segmentation based on the method of the maximal variance and improved genetic algorithm

Jianjia Pan; Lanyan Xue; Shenglin Zheng; Yuan Yan Tang

doi:10.1109/ICSMC.2007.4413905

Image segmentation based on the method of the maximal variance and improved genetic algorithm

Jianjia Pan^*
, Lanyan Xue
, Shenglin Zheng
, Yuan Yan Tang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Aiming for the problem of falling into local optimum when searching for the optimal threshold of the image using normal genetic algorithm, this paper presents a new method based on the maximal variance and improved genetic algorithm to segment the face image. This new method uses the maximal variance of the face gray image as the fitness and changes the problem of image segmentation into a problem of optimization. Adopting genetic algorithm which is characteristic of robustness and adaptability can increase efficiency. As a result, this new method can obtain the optimal segmentation result when applied to different face images. Experiments show that using this method to search for the global threshold can converge the optimal value and decrease the searching time

Original language	English
Title of host publication	2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Pages	2923-2926
Number of pages	4
ISBN (Electronic)	9781424409914
DOIs	https://doi.org/10.1109/ICSMC.2007.4413905
Publication status	Published - 7 Oct 2007
Event	2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 - Montreal, QC, Canada Duration: 7 Oct 2007 → 10 Oct 2007

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Publisher	IEEE
ISSN (Print)	1062-922X

Conference

Conference	2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007
Country/Territory	Canada
City	Montreal, QC
Period	7/10/07 → 10/10/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/ICSMC.2007.4413905

Cite this

Pan, J., Xue, L., Zheng, S., & Tang, Y. Y. (2007). Image segmentation based on the method of the maximal variance and improved genetic algorithm. In 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 (pp. 2923-2926). (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2007.4413905

@inproceedings{c8ffb33fa1c2407aa84f578c3d4edb08,

title = "Image segmentation based on the method of the maximal variance and improved genetic algorithm",

abstract = "Aiming for the problem of falling into local optimum when searching for the optimal threshold of the image using normal genetic algorithm, this paper presents a new method based on the maximal variance and improved genetic algorithm to segment the face image. This new method uses the maximal variance of the face gray image as the fitness and changes the problem of image segmentation into a problem of optimization. Adopting genetic algorithm which is characteristic of robustness and adaptability can increase efficiency. As a result, this new method can obtain the optimal segmentation result when applied to different face images. Experiments show that using this method to search for the global threshold can converge the optimal value and decrease the searching time",

author = "Jianjia Pan and Lanyan Xue and Shenglin Zheng and Tang, \{Yuan Yan\}",

note = "Publisher copyright: {\textcopyright} 2007 IEEE; 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007 ; Conference date: 07-10-2007 Through 10-10-2007",

year = "2007",

month = oct,

day = "7",

doi = "10.1109/ICSMC.2007.4413905",

language = "English",

isbn = "1424409918",

series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

publisher = "IEEE",

pages = "2923--2926",

booktitle = "2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007",

}

Pan, J, Xue, L, Zheng, S & Tang, YY 2007, Image segmentation based on the method of the maximal variance and improved genetic algorithm. in 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 2923-2926, 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007, Montreal, QC, Canada, 7/10/07. https://doi.org/10.1109/ICSMC.2007.4413905

Image segmentation based on the method of the maximal variance and improved genetic algorithm. / Pan, Jianjia; Xue, Lanyan; Zheng, Shenglin et al.
2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007. 2007. p. 2923-2926 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Image segmentation based on the method of the maximal variance and improved genetic algorithm

AU - Pan, Jianjia

AU - Xue, Lanyan

AU - Zheng, Shenglin

AU - Tang, Yuan Yan

PY - 2007/10/7

Y1 - 2007/10/7

N2 - Aiming for the problem of falling into local optimum when searching for the optimal threshold of the image using normal genetic algorithm, this paper presents a new method based on the maximal variance and improved genetic algorithm to segment the face image. This new method uses the maximal variance of the face gray image as the fitness and changes the problem of image segmentation into a problem of optimization. Adopting genetic algorithm which is characteristic of robustness and adaptability can increase efficiency. As a result, this new method can obtain the optimal segmentation result when applied to different face images. Experiments show that using this method to search for the global threshold can converge the optimal value and decrease the searching time

AB - Aiming for the problem of falling into local optimum when searching for the optimal threshold of the image using normal genetic algorithm, this paper presents a new method based on the maximal variance and improved genetic algorithm to segment the face image. This new method uses the maximal variance of the face gray image as the fitness and changes the problem of image segmentation into a problem of optimization. Adopting genetic algorithm which is characteristic of robustness and adaptability can increase efficiency. As a result, this new method can obtain the optimal segmentation result when applied to different face images. Experiments show that using this method to search for the global threshold can converge the optimal value and decrease the searching time

UR - https://www.scopus.com/pages/publications/40949154699

U2 - 10.1109/ICSMC.2007.4413905

DO - 10.1109/ICSMC.2007.4413905

M3 - Conference proceeding

AN - SCOPUS:40949154699

SN - 1424409918

SN - 9781424409907

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 2923

EP - 2926

BT - 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

T2 - 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

Y2 - 7 October 2007 through 10 October 2007

ER -

Image segmentation based on the method of the maximal variance and improved genetic algorithm

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