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| Fuzzy Clustering Image Segmentation Based on Neighborhood Constrained Gaussian Mixture Model |
| ZHAO Quanhua, ZHANG Hongyun, ZHAO Xuemei, LI Yu |
| Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000 |
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Abstract The characteristics of data can not be simulated in the traditional fuzzy clustering method effectively. Gaussian mixture model with neighbor constraints is introduced to solve the problem. Gaussian distribution is used to characterize the statistical characteristics of spectral measure. The correlation between the pixels and their neighborhood pixels are defined as prior probability and used as weight coefficients of each component in Gaussian mixture model. Finally, a Gaussian mixture model with neighborhood constraints in feature field is constructed. Log weighted Gaussian component in the mixture model is used as dissimilar measurement between the pixels and clusters, and a fuzzy clustering objective function is constructed based on Gaussian mixture model. Neighborhood constraints are introduced as a weight of component in traditional Gaussian mixture model and combined with fuzzy clustering method. Thus, the problem of multi-peak distribution of data is solved. Finally, the accuracy of the proposed algorithm is verified by experiments.
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Received: 28 September 2016
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| Fund:Supported by General Program of National Natural Science Foundation of China(No.41271435), Young Scientists Fund of National Natural Science Foundation of China(No.41301479), Natural Science Foundation of Liaoning Province(No.2015020090) |
About author:: ZHAO Quanhua(Corresponding author),born in 1978, Ph.D., associate professor. Her research interests include the modeling and analysis of remote sensing image and the application of random geometry in remote sensing image processing.)
ZHANG Hongyun,born in 1992,master student. Her research interests include remote sensing information extraction.)
ZHAO Xuemei, born in 1989, Ph.D. candidate. Her research interests include the application of fuzzy mathematics in the modeling and analysis of remote sensing data.)
LI Yu, born in 1963, Ph.D., professor. His research interests include remote sensing data processing theory and basic application research.) |
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[1] 郭 瑞.基于模糊聚类的图像分割方法的研究.硕士学位论文.长春:吉林大学, 2015.
(GUO R. Research on the Segmentation Method Based on Fuzzy Clustering. Master Dissertation. Changchun, China: Jilin University, 2015.)
[2] BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, USA: Kluwer Academic Publishers, 1981.
[3] 赵雪梅,李 玉,赵泉华.基于隐马尔可夫高斯随机场模型的模糊聚类高分辨率遥感影像分割算法.电子学报, 2016, 44(3): 679-686.
(ZHAO X M, LI Y, ZHAO Q H. Hidden Markov Gaussian Random Field Based Fuzzy Clustering Algorithm for High-Resolution Remote Sensing Image Segmentation. Acta Electronica Sinica, 2016, 44(3): 679-686.)
[4] 肖满生,文志诚,张居武,等.一种改进隶属度函数的FCM聚类算法.控制与决策, 2015, 30(12): 2270-2274.
(XIAO M S, WEN Z C, ZHANG J W, et al. An FCM Clustering Algorithm with Improved Membership Function. Control and Decision, 2015, 30(12): 2270-2274.)
[5] 赵泉华,李晓丽,赵雪梅,等.基于空间约束Student′s-T混合模型的模糊聚类图像分割.控制与决策, 2016, 31(11): 2065-2070.
(ZHAO Q H, LI X L, ZHAO X M, et al. Fuzzy Clustering Algorithm Based on Spatially Constrained Student′s-T Mixture Model for Image Segmentation. Control and Decision, 2016, 31(11): 2065-2070.)
[6] 王 鑫.基于高斯混合模型的聚类算法及其在图像分割中的应用.硕士学位论文.太原:中北大学, 2013.
(WANG X. The Clustering Algorithm and Its Application in Image Segmentation Based on Gaussian Mixture Model. Master Dissertation. Taiyuan, China: North University of China, 2013.)
[7] 张媛媛,钟意伟.基于变分高斯混合模型的图像分割算法.宁波大学学报(理工版), 2014, 27(1): 23-28.
(ZHANG Y Y, ZHONG Y W. Image Segmentation via Variational Mixture of Gaussians. Journal of Ningbo University (Natural Science & Engineering), 2014, 27(1): 23-28.)
[8] 田 杰,韩 冬,胡秋霞,等.基于PCA和高斯混合模型的小麦病害彩色图像分割.农业机械学报, 2014, 45(7): 267-271.
(TIAN J, HAN D, HU Q X, et al. Segmentation of Wheat Rust Lesion Image Using PCA and Gaussian Mixture Model. Journal of Agricultural Machinery, 2014, 45(7): 267-271.)
[9] 向 晶,周绍光,陈 超.基于改进高斯混合模型的遥感影像道路提取.测绘工程, 2014, 23(3): 42-45.
(XIANG J, ZHOU S G, CHEN C. Road Extraction of High-Resolution Remote Sensing Images with Improved Gaussian Mixture Model. Engineering of Surveying and Mapping, 2014, 23(3): 42-45.)
[10] MCLACHLAN G J, PEEL D. Finite Mixture Models. New York, USA: Wiley, 2000.
[11] LI S Z. Markov Random Field Modeling in Image Analysis. London, UK: Springer-Verlag, 2009.
[12] 曹家梓,宋爱国.基于马尔科夫随机场的纹理图像分割方法研究.仪器仪表学报, 2015, 36(4): 776-786.
(CAO J Z, SONG A G. Research on the Texture Image Segmentation Method Based on Markov Random Field. Chinese Journal of Scientific Instrument, 2015, 36(4): 776-786.)
[13] HOU Y M, SUN X L, LUN X M, et al. Gaussian Mixture Model Segmentation Algorithm for Remote Sensing Image // Proc of the International Conference on Machine Vision and Human-Machine Interface. Washington, USA: IEEE, 2010: 275-278.
[14] SANJAY-GOPAL S, HEBERT T J. Bayesian Pixel Classification Using Spatially Variant Finite Mixtures and the Generalized EM Algorithm. IEEE Transactions on Image Processing, 1998, 7(7): 1014-1028.
[15] 申小虎,吕导中,万荣春.空间可变有限混合模型.中国图像图形学报, 2014, 19(12): 1820-1828.
(SHEN X H, L D Z, WAN R C. Spatially Variant Finite Mixture Model. Journal of Image and Graphics, 2014, 19(12): 1820-1828.)
[16] MIYAMOTO S, MUKAIDONO M. Fuzzy C-means as a Regularization and Maximum Entropy Approach // Proc of the 7th International Fuzzy Systems Association World Congress. New York, USA: ACM, 1997, II: 86-92.
[17] ICHIHASHI H, MIYAGISHI K, HONDA K. Fuzzy C-means Clustering with Regularization by K-L Information // Proc of the 10th IEEE International Conference on Fuzzy Systems. Washington, USA: IEEE, 2001, II: 924-927.
[18] NGUYEN T M, WU Q M J. Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(4): 621-635. |
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2017, Vol. 30 
