Post Segmentation of Fuzzy Clustering Algorithm

ZHU Zhan-long; ZHENG Yi-bo; WANG Yuan

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Post Segmentation of Fuzzy Clustering Algorithm

更新时间：2026-09-04

- Post Segmentation of Fuzzy Clustering Algorithm
- Vol. 35, Issue 3, Pages: 68-73(2018)
- 作者机构：
  
  1. 河北地质大学机构信息工程学院,河北,石家庄,050031
  2. 河北地质大学河北省光电信息与地球探测技术重点实验室机构,河北,石家庄,050031
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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朱占龙, 郑一博, 王远. 模糊聚类图像分割后处理[J]. 微电子学与计算机, 2018, 35(3): 68-73.

ZHU Zhan-long, ZHENG Yi-bo, WANG Yuan. Post Segmentation of Fuzzy Clustering Algorithm[J]. 2018, 35(3): 68-73.
朱占龙, 郑一博, 王远. 模糊聚类图像分割后处理[J]. 微电子学与计算机, 2018, 35(3): 68-73. DOI：

ZHU Zhan-long, ZHENG Yi-bo, WANG Yuan. Post Segmentation of Fuzzy Clustering Algorithm[J]. 2018, 35(3): 68-73. DOI：

摘要

由于采用模糊聚类算法进行图像分割存在错分点的情况

需要对错分点做进一步处理以提高分割效果

对此提出一种图像分割后处理方法.首先对模糊聚类算法得到的分割结果进行检测

找出可能的错分点

然后通过统计错分点周边的隶属度分布情况对其进行重新分类

最后通过加噪的人工合成图像及自然图像对提出的后处理方法进行实验验证.结果表明该方法不仅能够进一步提升模糊聚类算法的分割准确率

而且所分割的图像视觉效果也有很大程度的提升.

Abstract

Potentially misclassified pixels often display when the fuzzy clustering algorithm is used for image segmentation

so a post segmentation method corresponding to refining the segmentation results is proposed. The first step is the detection of segmentation results from the fuzzy clustering algorithms

and then it is natural to find and reclassify the potentially misclassified pixels by evaluating the membership matrix of neighborhood window. The last step is the experimental verification via several test images including both synthetic image and natural images. The results of the experiment demonstrate that the proposed method not only improves the segmentation accuracy of fuzzy clustering algorithm

but also promotes the visual effect of segmentation to some extent.

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Keywords

references

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