融合改进人工蜂群和K均值聚类的图像分割

赵文昌; 李忠木

doi:10.3788/YJYXS20173209.0726

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融合改进人工蜂群和K均值聚类的图像分割

图像处理 | 更新时间：2020-07-07

- 融合改进人工蜂群和K均值聚类的图像分割
- Image segmentation algorithm based on improved artificial bee colony and K-mean clustering
- 液晶与显示 2017年32卷第9期页码：726-735
- 作者机构：
  
  大理大学工程学院, 云南大理 671003
- 作者简介：
  
  [ "赵文昌(1979-), 男, 云南剑川人, 硕士, 讲师, 主要从事图形图像处理与模式识别方面的研究。Email:zhaowenchang79@126.com" ]
  [ "李忠木(1980-), 男, 云南楚雄人, 博士, 教授, 主要从事天体物理与图像处理方面的研究" ]
- 基金信息：
  
  云南省教育厅科研项目(2014Y409);Educational Scientific Research Projects of Yunnan Province(2014Y409)
- DOI：10.3788/YJYXS20173209.0726
  中图分类号： TP391.41
- 收稿日期：2017-04-05，
  
  录用日期：2017-5-23，
  
  纸质出版日期：2017-09-05
- 稿件说明：
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赵文昌, 李忠木. 融合改进人工蜂群和K均值聚类的图像分割[J]. 液晶与显示, 2017,32(9):726-735.

Wen-chang ZHZO, Zhong-mu LI. Image segmentation algorithm based on improved artificial bee colony and K-mean clustering[J]. Chinese journal of liquid crystals and displays, 2017, 32(9): 726-735.
赵文昌, 李忠木. 融合改进人工蜂群和K均值聚类的图像分割[J]. 液晶与显示, 2017,32(9):726-735. DOI： 10.3788/YJYXS20173209.0726.

Wen-chang ZHZO, Zhong-mu LI. Image segmentation algorithm based on improved artificial bee colony and K-mean clustering[J]. Chinese journal of liquid crystals and displays, 2017, 32(9): 726-735. DOI： 10.3788/YJYXS20173209.0726.

摘要

针对人工蜂群优化的

均值算法易陷入局部最优、搜索精度不够、分割图像不够细致等问题，本文融合自适应人工蜂群和

均值聚类，提出了一种新的图像分割算法。算法首先利用距离最大最小乘积对种群进行初始化；其次采用自适应搜索参数动态调整邻域搜索范围，使人工蜂群算法快速收敛于全局最优；然后将人工蜂群输出的所有蜜源进行

均值聚类，克服

均值聚类结果对初始聚类中心的依赖，再将聚类划分结果进行Powell局部搜索，加快算法收敛的速度，将得到的新聚类中心更新蜂群中蜜源位置。最后，将本文算法与其他两种同类分割算法进行试验对比。实验结果表明：与其他两种算法相比，本文提出的分割算法在保证运行时间的前提下，分割准确率比其他两种算法分别至少提高了3.5%和4.8%，表现出了较高的分割质量。

Abstract

In order to overcome the artificial colony optimization

-means which be fallen into local optimum easily

converged slowly

segmented roughly and other issues

a new image segmentation algorithm is proposed based on adaptive artificial bee colony and

-mean clustering. First

the population is initialized by the maximum and minimum product; Secondly

adaptive search parameters are used to adjust neighborhood search scope dynamically

that makes artificial bee colony algorithm quickly converge to global optimal and achieve a more optimal solution; Then

all nectaries will be clustered by

-mean to the dependence of clustering result on the initial center

and then clustering results are divided into Powell local search

which accelerate the algorithm convergence speed

that will receive a new clustering center update colony of nectar source location. Finally

the proposed algorithm is compared with the other two algorithms. The experimental results show that compared with the other two algorithms

the segmentation algorithm proposed in this paper can improve the segmentation accuracy by at least 3.5% and 4.8%

respectively

under the premise of guaranteeing the running time

showing a higher segmentation quality.

关键词

Keywords

references

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