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

Abstract

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

CLC Number:

TP391.41

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.

Figures/Tables 13

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

References 27

[1]	徐鹏飞, 张菁, 尹腾飞 ,等. 基于改进 PCNN 算法的电力设备图像分割研究[J]. 智能计算机与应用, 2019,9(3): 59-62,68.
	XU P F , ZHANG J , YIN T F ,et al. Research on image segmentation of power equipment based on improved PCNN algorithm[J]. Intelligent Computer and Application, 2019,9(3): 59-62,68.
[2]	余成波, 曾亮, 张林 . 基于OTSU和区域生长的电气设备多点故障分割[J]. 红外技术, 2018,40(10): 1008-1012.
	YU C B , ZENG L , ZHANG L . Multi-point fault segmentation of elec-trical equipment based on OTSU and regional growth[J]. Infrared Technology, 2008,40(10): 1008-1012.
[3]	王智杰, 牛硕丰, 刘相兴 ,等. 蝙蝠算法优化二维熵的变电设备红外图像分割应用研究[J]. 电子设计工程, 2018,26(18): 83-87.
	WANG Z J , NIU S F , LIU X X ,et al. Study on application of bat algo-rithm to optimize two-dimensional entropy for infrared image seg-mentation of transformer equipment[J]. Electronic Design Engineer-ing, 2008,26(18): 83-87.
[4]	李鑫, 崔昊杨, 霍思佳 ,等. 基于粒子群优化法的Niblack电力设备红外图像分割[J]. 红外技术, 2018,40(8): 780-785.
	LI X , CUI H Y , HUO S J ,et al. Infrared image segmentation of Nib-lack power equipment based on particle swarm optimization[J]. Infra-red Technology, 2014,40(8): 780-785.
[5]	KAPIL S , CHAWLA M , ANSARI M D . On K-means data clustering algorithm with genetic algorithm[C]// Fourth International Conference on Parallel,Distributed and Grid Computing. Piscataway:IEEE Press, 2016: 202-206.
[6]	ATANASSOV K T . Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 1986,20(1): 87-96.
[7]	PELEKIS N , IAKOVIDIS D K , KOTSIFAKOS E E ,et al. Fuzzy Clustering of Intuitionistic Fuzzy Data[J]. International Journal of Business Intelligence and Data Mining, 2008,3(1): 45-65.
[8]	XU Z , WU J . Intuitionistic fuzzy C-means clustering algorithms[J]. Journal of Systems Engineering and Electronics, 2010,21(4): 580-90.
[9]	LIN K . A Novel Evolutionary Kernel Intuitionistic Fuzzy C-means Clustering Algorithm[J]. IEEE Transactions on Fuzzy Systems, 2014,22(5): 1074-1087.
[10]	DANISH LOHANI Q M , SOLANKI R , MUHURI P K . Novel adaptive clustering algorithms based on a probabilistic similarity measure over atanassov intuitionistic fuzzy set[J]. IEEE Transactions on Fuzzy Systems, 2018,26(6): 3715-3729.
[11]	ZADEH L A . Fuzzy sets[J]. Information and Control, 1965,8(3): 338-353.
[12]	MENDEL J M . Uncertain rule-based fuzzy logic systems:introduction and new directions[M]. New Jersey: Prentice HallPress, 2001.
[13]	MENDEL J M . Computing derivatives in interval type-2 fuzzy logic systems[J]. IEEE Transactions on Fuzzy Systems, 2004,12(1): 84-98.
[14]	ATANASSOV K T . Intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 1986,20(1): 87-96.
[15]	DUNN J C . A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973,3(3): 32-57.
[16]	BEZDEK J C . Fuzzy mathematics in pattern classification[D]. Ithaca:Cornell University, 1973: 142-147.
[17]	SUGENO M . Fuzzy measures and fuzzy integrals–a survey[J]. Readings in Fuzzy Sets for Intelligent Systems, 1993,6(1): 251-257.
[18]	WANG W , XIN X . Distance measure between intuitionistic fuzzy sets[J]. Pattern Recognition Letters, 2005,26(13): 2063-2069.
[19]	SZMIDT E , KACPRZYK J . Distances between intuitionistic fuzzy sets[J]. Fuzzy Sets and Systems, 2000,114: 505-518.
[20]	WEST G A , CLARK T A . A survey and examination of subpixel measurement techniques[J]. Proceedings of SPIE-The International Society for Optical Engineering, 1990,1395(3): 456-462.
[21]	BAI X , CHEN Z , ZHANG Y ,et al. Infrared ship target segmentation based on spatial information improved FCM[J]. IEEE Transactions on Cybernetics, 2016,46(12): 3259-3271.
[22]	ZHU H , LU L , FAN Y ,et al. Parallel implementation of the FLICM algorithm for SAR image change detection on intel MIC[C]// 2016 IEEE International Geoscience and Remote Sensing Symposium. Piscataway:IEEE Press, 2016: 2340-2343.
[23]	LIN K , HUNG K , LIN C . Rule generation based on novel kernel intuitionistic fuzzy rough set model[J]. IEEE Access, 2018(6): 11953-11958.
[24]	KAUSHAL M , SOLANKI R , LOHANI Q M D ,et al. A novel intuitionistic fuzzy set generator with application to clustering[C]// 2018 IEEE International Conference on Fuzzy Systems. Piscataway:IEEE Press, 2018: 1-8.
[25]	LIU Z , ZHOU F , CHEN X ,et al. Iterative infrared ship target segmentation based on multiple features[J]. Pattern Recognition, 2014,47(9): 2839-2852.
[26]	BAI X , WANG Y , LIU H ,et al. Symmetry information based fuzzy clustering for infrared pedestrian segmentation[J]. IEEE Transactions on Fuzzy Systems, 2018,26(4): 1946-1959.
[27]	FAN Z , WANG C , MA X . Double-threshold image segmentation method based on gray gradient[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2009(7506): 20-28.

Metrics

Recommended 0

No Suggested Reading articles found!

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

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 27

Related Articles 2

Metrics

Recommended 0

[1]	Bin LIU,Ya-jing SUN,Hao-ran LIU,Rong-rong YIN. Key node decision algorithm based on local information and energy factor in wireless sensor network [J]. Journal on Communications, 2015, 36(10): 9-16.
[2]	Zu-xi WANG,Zhao-zhang DENG,Li LI. Fair and efficient network congestion control algorithm based on minority game with local information [J]. Journal on Communications, 2014, 35(1): 148-155.