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Image Segmentation Based on Modified Particle Swarm Optimization and Fuzzy C-Means Clustering Algorithm

机译:基于改进粒子群算法和模糊C-均值聚类算法的图像分割

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In order to solve the problems of the fuzzy C-meaus (FCM) clustering algorithm when it is applied to the image segmentation such as making itself easily traps into local optimum and huge calculation,an image segmentation algorithm based on the modified particle swarm optimization(MPSO)and FCM clustering algorithm is proposed.The simulation results and the comparison between the proposed algorithm and FCM algorithm indicate that the proposed algorithm can obtain better segmentation effects and excel the existing FCM algorithm in several performance,such as the average dispersion,the maximum intra-distance between pixel and their cluster center,and the minimum interdistance between any pair of clusters.
机译:为了解决模糊C-meaus(FCM)聚类算法在图像分割中的问题,使其易于陷入局部最优且计算量大的问题,提出了一种基于改进粒子群算法的图像分割算法(仿真结果以及与FCM算法的比较表明,该算法可获得较好的分割效果,在平均色散,最大值等方面表现优于现有的FCM算法。像素与其群集中心之间的距离以及任何一对群集之间的最小距离。

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