scholarly journals A image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering

2015 ◽  
Vol 12 (2) ◽  
pp. 873-893 ◽  
Author(s):  
Jiansheng Liu ◽  
Shangping Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

2015 ◽  
Vol 23 (05) ◽  
pp. 667-683 ◽  
Author(s):  
Troudi Ahmed ◽  
Bouzbida Mohamed ◽  
Chaari Abdelkader
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Kernel Method ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

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