基于分布信息直觉模糊c均值聚类的红外图像分割算法

doi:10.11959/j.issn.1000-436x.2020071

通信学报 ›› 2020, Vol. 41 ›› Issue (5): 120-129.doi: 10.11959/j.issn.1000-436x.2020071

基于分布信息直觉模糊c均值聚类的红外图像分割算法

王晓飞,胡凡奎,黄硕

黑龙江大学电子工程学院，黑龙江哈尔滨 150080

修回日期:2020-03-11 出版日期:2020-05-25 发布日期:2020-05-30
作者简介:王晓飞（1977- ），男，黑龙江哈尔滨人，博士，黑龙江大学副教授，主要研究方向为高光谱数据分析和图像处理、多源信息融合、模式识别和分类|胡凡奎（1996- ），男，山东济宁人，黑龙江大学硕士生，主要研究方向为遥感图像处理|黄硕（1994- ），男，黑龙江哈尔滨人，黑龙江大学硕士生，主要研究方向为遥感图像处理
基金资助:
国家自然科学基金资助项目(61871150);国家重点研发计划基金资助项目(2016YFB0502502)

Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering

Xiaofei WANG,Fankui HU,Shuo HUANG

Electronic Engineering College,Heilongjiang University,Harbin 150080

Revised:2020-03-11 Online:2020-05-25 Published:2020-05-30
Supported by:
The National Natural Science Foundation of China(61871150);The National Key Research and Development Program of China(2016YFB0502502)

摘要/Abstract

摘要：

针对传统的直觉模糊c均值聚类算法进行图像分割时对聚类中心敏感导致最终聚类精度低、细节保留性差、时间复杂度较大等不足，提出了一种适用于电力设备红外图像分割的基于分布信息的直觉模糊c均值聚类算法。红外图像中高强度的非目标对象与图像强度不均匀对图像分割有较强干扰，所提算法能有效抑制该干扰。首先，将高斯模型引入电力设备的全局空间分布信息中以改进IFCM算法；其次，利用局部空间信息的空间算子优化隶属函数来解决边缘模糊和图像强度不均匀问题。经过对Terravic动态红外数据库与包含300 幅电力设备红外图像的数据集进行实验，相对区域错误率在10%左右，受模糊因子m变化影响较小，验证了所提算法在有效性与适用性上明显优于其他对比算法。

关键词: 直觉模糊c均值聚类, 红外图像, 高斯模型, 局部信息

Abstract:

Due to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation,which resulted in the low clustering precision,poor retention of details,and large time complexity,an intuitionistic fuzzy c-means clustering algorithm was proposed based on spatial distribution information suitable for infrared image segmentation of power equipment.The non-target objects with high intensity and the non-uniformity of image intensity in the infrared image had strong interference to the image segmentation,which could be effectively suppressed by the proposed algorithm.Firstly,the Gaussian model was introduced into the global spatial distribution information of power equipment to improve the IFCM algorithm.Secondly,the membership function was optimized by local spatial operator to solve the problem of edge blur and image intensity inhomogeneity.The experiments conducted on Terravic motion IR database and the data set containing 300 infrared images of power equipment show that,the relative region error rate is about 10% and is less affected by the change of fuzzy factor m.The effectiveness and applicability of the proposed algorithm are superior to other comparison algorithms.

Key words: intuitionistic fuzzy c-means clustering, infrared image, Gaussian model, local information

中图分类号:

TP391.41

王晓飞,胡凡奎,黄硕. 基于分布信息直觉模糊c均值聚类的红外图像分割算法[J]. 通信学报, 2020, 41(5): 120-129.

Xiaofei WANG,Fankui HU,Shuo HUANG. Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering[J]. Journal on Communications, 2020, 41(5): 120-129.

图/表 13

图1

图2

图3

图4

表1

参数及其含义"

符号	具体含义	取值
m	模糊因子	2
β	可能区域的固定值	0.1
λ	直觉模糊补充参数	1.2
$\frac{σ}{α}$	中心距离权重	4 000
γ	中心距离差权重	10^-4
?	局部分布信息权重	2.5

表1

图5

图6

图7

图8

图9

图10

图11

表2

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算法	平均时间/s
本文算法	6.527 9
FCM算法	6.416 7
KIFCM算法	9.849 1
GQFCM算法	1.675 4
FLICM算法	49.125 2
PIFCM算法	27.722 5
IFCM算法	3.496 7

基于分布信息直觉模糊c均值聚类的红外图像分割算法

Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering

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