计算机科学 ›› 2009, Vol. 36 ›› Issue (7): 278-280.doi: 10.11896/j.issn.1002-137X.2009.07.068

• 图形图像及体系结构 • 上一篇    下一篇

基于全局优化搜索算法的图像分割研究

杨丹,瞿中   

  1. (重庆大学计算机学院 重庆400030);(重庆邮电大学计算机科学与技术学院 重庆400065)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受重庆市科委自然科学基金计划资助项目(No. CSTC-2007BB2451)资助。

Research on Image Segmentation Based on Global Optimization Search Algorithm

YANG Dan,QU Zhong   

  • Online:2018-11-16 Published:2018-11-16

摘要: 基于聚类的图像分割算法中,由于模糊C-均值算法需要初始化,并且目标函数存在许多局部极小点,如果初始化落在目标函数的局部极小点附近,就会造成算法收敛到局部极小。为了解决此问题,采用全局优化搜索算法,提出了将全局优化搜索技术引入进来对模糊C-均值算法加以改进,分析了在不同初始条件下,对许多样本的聚类分析时,全局优化搜索算法比传统的模糊C-均值聚类算法更加有效,通过仿真实验验证并对算法性能进行理论分析。

关键词: 全局优化搜索,图像分割,模糊聚类,模糊C-均值算法,硬C-均值算法

Abstract: In the cluster-based image segmentation algorithm,the initialization was needed in FCM(fuzzy C-means) algorithm and there were lots of local minimum in the objective function,if the initialization abtained the local minimum vicinity point, it would cause a convergence to local minimum. In order to solve this problem, a global optimization search algorithm was introduced to the FCM algorithm because it has the global optimization search capabilities. The improved FCM has more effective than the traditional method of FCM clustering algorithm through the simulation experiments and theoretical analysis of algorithm performance.

Key words: Global optimization search, Image segmentation, Fuzzy clustering, Fuzzy C-means algorithm, Hard C-means algorithm

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