基于改进遗传算法(MfGA)的模糊c均值聚类的多级彩色图像分割

2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2016-09-01 DOI:10.1109/ICRCICN.2016.7813635

Sunanda Das, S. De

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引用次数: 10

摘要

收敛到局部极小点是传统模糊c均值算法的主要缺点之一。由于这一缺点，分割结果可能会因为没有正确选择聚类质心而受到影响。为了克服这个问题，提出了一种改进的遗传算法(MfGA)来提高FCM的性能。从MfGA中得到的优化类水平被用作FCM的初始输入，用于在大搜索空间中寻找全局最优解。在两幅多层彩色图像上，对该方法进行了广泛的性能比较，证明了该方法的优越性。

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Multilevel color image segmentation using modified genetic algorithm (MfGA) inspired fuzzy c-means clustering

Convergence to local minima point is one of the major disadvantages of conventional fuzzy c-means (FCM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overcome this, a modified genetic (MfGA) algorithm is proposed to improve the performance of FCM. The optimized class levels derived from the MfGA are employed as initial input to FCM for finding global optimal solutions in a large search space. An extensive performance comparison of the proposed MfGA inspired conventional FCM and GA based FCM on two multilevel color images establishes the superiority of the proposed approach.

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2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

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