10 April 2018 An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061525 (2018) https://doi.org/10.1117/12.2302922
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
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.
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Mingwei Wang, Mingwei Wang, Youchuan Wan, Youchuan Wan, Xianjun Gao, Xianjun Gao, Zhiwei Ye, Zhiwei Ye, Maolin Chen, Maolin Chen, } "An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061525 (10 April 2018); doi: 10.1117/12.2302922; https://doi.org/10.1117/12.2302922
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