Geodesic
A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering
Computer Science and Information Systems, Tome 12 (2015) no. 2.

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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.
Keywords: differential evolution particle swarm optimization, fuzzy c-means clustering, image segmentation, HSI color space 
@article{CSIS_2015_12_2_a27,
     author = {Jiansheng Liu and Shangping Qiao},
     title = {A {Image} {Segmentation} {Algorithm} {Based} on {Differential} {Evolution} {Particle} {Swarm} {Optimization} {Fuzzy} {C-Means} {Clustering}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {12},
     number = {2},
     year = {2015},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2015_12_2_a27/}
}
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Jiansheng Liu; Shangping Qiao. A Image Segmentation Algorithm Based on Differential Evolution Particle Swarm Optimization Fuzzy C-Means Clustering. Computer Science and Information Systems, Tome 12 (2015) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2015_12_2_a27/