基于近邻搜索花授粉优化的直觉模糊聚类图像分割

赵凤; 孙文静; 刘汉强; 曾哲

doi:10.11999/JEIT190428

基于近邻搜索花授粉优化的直觉模糊聚类图像分割

doi: 10.11999/JEIT190428

赵凤^{1, 2, ,},
孙文静^{1, 2},
刘汉强³,
曾哲^{1, 2}

1.
西安邮电大学通信与信息工程学院西安 710121
2.
西安邮电大学电子信息现场勘验应用技术公安部重点实验室西安 710121
3.
陕西师范大学计算机科学学院西安 710119

基金项目: 国家自然科学基金(61571361, 61671377, 61102095)，西安邮电大学西邮新星团队基金(xyt2016-01)

详细信息

作者简介:
赵凤：女，1980年生，教授，研究方向为计算智能与图像处理

孙文静：女，1995年生，硕士生，研究方向为图像处理

刘汉强：男，1981年生，副教授，研究方向为模式识别与图像处理

曾哲：男，1995年生，硕士生，研究方向为图像处理

通讯作者:
赵凤　fzhao.xupt@gmail.com

中图分类号: TP391
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出版历程
- 收稿日期: 2019-06-11
- 修回日期: 2019-12-09
- 网络出版日期: 2019-12-20
- 刊出日期: 2020-06-04

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

Feng ZHAO^{1, 2
, ,},
Wenjing SUN^{1, 2},
Hanqiang LIU³,
Zhe ZENG^{1, 2}

1.
School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Key Laboratory of Electronic Information Application Technology for Scene Investigation of Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
3.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China

Funds: The National Natural Science Foundation of China (61571361, 61671377, 61102095), The New Star Team Foundation of Xi’an University of Posts & Telecommunications (xyt2016-01)

摘要

摘要:
为克服传统模糊聚类算法应用于图像分割时，易受噪声影响，对聚类中心初始值敏感，易陷入局部最优，模糊信息处理能力不足等缺陷，该文提出基于近邻搜索花授粉优化的直觉模糊聚类图像分割算法。首先设计一种新颖的图像空间信息提取策略，进而构造融合图像空间信息的直觉模糊聚类目标函数，提高对于噪声的鲁棒性，提升算法处理图像中模糊信息的能力。为了优化上述目标函数，提出一种基于近邻学习搜索机制的花授粉算法，实现对于聚类中心的寻优，解决对于聚类中心初始值敏感，易陷入局部最优的问题。实验结果表明所提算法能在多种噪声图像上取得令人满意的分割效果。
- 图像分割 /
- 直觉模糊聚类 /
- 花授粉优化 /
- 空间信息 /
- 近邻学习
Abstract:
In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as that are easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.
- Image segmentation /
- Intuitionistic fuzzy clustering /
- Flower pollination optimization /
- Spatial information /
- Nearest neighbor learning

HTML全文

图 1 各空间信息抗噪性能对比

下载: 全尺寸图片幻灯片

图 2 算法聚类准确率各类型噪声下随α变化结果

下载: 全尺寸图片幻灯片

图 3 算法聚类准确率各类型噪声下随β变化结果

下载: 全尺寸图片幻灯片

图 4 #241004的高斯噪声图像分割结果

下载: 全尺寸图片幻灯片

图 5 #241004的椒盐噪声图像分割结果

下载: 全尺寸图片幻灯片

图 6 #241004的混合噪声图像分割结果

下载: 全尺寸图片幻灯片

表 1 各算法聚类准确率对比

图像	噪声水平	IFCM	FPA-FCM	FLICM	IIFCM	NDFCM	本文算法
	高斯	0.7536	0.7376	0.9306	0.7646	0.9279	0.9284
#113016	椒盐	0.8254	0.8320	0.9019	0.8268	0.9119	0.9290
	高斯&椒盐	0.7806	0.7443	0.9163	0.7806	0.9054	0.9175
	高斯	0.8373	0.8357	0.9054	0.8234	0.8945	0.8986
#101027	椒盐	0.7962	0.7939	0.8586	0.8041	0.8857	0.8913
	高斯&椒盐	0.7806	0.7809	0.8834	0.7782	0.8839	0.8964
	高斯	0.5640	0.5669	0.9101	0.5640	0.9112	0.8979
#241004	椒盐	0.6725	0.6725	0.6462	0.6725	0.8662	0.9116
	高斯&椒盐	0.5383	0.4847	0.6487	0.5442	0.8408	0.9012
	高斯	0.8346	0.7888	0.9329	0.8570	0.9323	0.9332
#15088	椒盐	0.8416	0.8395	0.9321	0.8421	0.9306	0.9331
	高斯&椒盐	0.8225	0.7989	0.9326	0.8263	0.9285	0.9329
	高斯	0.7719	0.8329	0.8883	0.6360	0.8806	0.8962
#296059	椒盐	0.7500	0.4822	08319	0.6671	0.8654	0.9022
	高斯&椒盐	0.6975	0.2714	0.8530	0.6078	0.8582	0.8938

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