吴成茂;

• 1:西安邮电大学电子工程学院

摘要(Abstract)：

模糊C-均值聚类(fuzzy C-means clustering,FCM)是一种揭示数据内在结构的重要工具之一,其具有良好的扩展性、解释性、准确性和稳定性,已广泛应用于模式分析与机器智能等众多领域。首先,对FCM应用于图像分割研究所取得的进展进行系统梳理和分类;其次,从聚类目标函数构造的距离度量和正则化约束等角度出发,分析和讨论不同类型鲁棒模糊聚类的分割方法,指出其优缺点和应用现状;依据图像分割结构的不适定性,揭示现有的鲁棒模糊聚类分割算法的构造机理和差分演化动力学特性;最后,根据当前深度学习、微分拓扑、代数几何、共形几何代数、信息几何和忆阻神经形态计算等理论展望未来鲁棒模糊聚类分割方法的发展趋势和应用前景。

关键词(KeyWords)： 图像分割;模糊C-均值聚类;空间局部信息;鲁棒性

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 吴成茂;

Email:

DOI: 10.13682/j.issn.2095-6533.2020.06.001

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