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An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
Author(s): Mingwei Wang; Youchuan Wan; Xianjun Gao; Zhiwei Ye; Maolin Chen
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Paper Abstract

Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Paper Details

Date Published: 10 April 2018
PDF: 6 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061525 (10 April 2018); doi: 10.1117/12.2302922
Show Author Affiliations
Mingwei Wang, Wuhan Univ. (China)
Youchuan Wan, Wuhan Univ. (China)
Xianjun Gao, Yangtze Univ. (China)
Zhiwei Ye, Hubei Univ. of Technology (China)
Maolin Chen, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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