fuzzy c-means clustering based on improved marked watershed transformation

被引：0

作者：

Zhao C. ^{[1
,2
]}

Zhao H. ^{[1
]}

Yao W. ^{[3
]}

机构：

[1] School of Electronic and Information Engineering, Hebei University of Technology, Tianjin

[2] Tianjin University of Finance and Economics Pearl River College, Tianjin

[3] School of Economics and Management, Tianjin University of Science and Technology, Tianjin

来源：

Telkomnika (Telecommunication Computing Electronics and Control) | 2016年 / 14卷 / 03期

关键词：

Adaptive median filtering; Fuzzy C-Means clustering; Fuzzy similarity relation; Marked watershed segmentation;

D O I：

10.12928/TELKOMNIKA.v14i3.2757

中图分类号：

学科分类号：

摘要：

Currently, the fuzzy c-means algorithm plays a certain role in remote sensing image classification. However, it is easy to fall into local optimal solution, which leads to poor classification. In order to improve the accuracy of classification, this paper, based on the improved marked watershed segmentation, puts forward a fuzzy c-means clustering optimization algorithm. Because the watershed segmentation and fuzzy c-means clustering are sensitive to the noise of the image, this paper uses the adaptive median filtering algorithm to eliminate the noise information. During this process, the classification numbers and initial cluster centers of fuzzy c-means are determined by the result of the fuzzy similar relation clustering. Through a series of comparative simulation experiments, the results show that the method proposed in this paper is more accurate than the ISODATA method, and it is a feasible training method. © 2016 Universitas Ahmad Dahlan.

引用

页码：981 / 986

页数：5

共 9 条

[1]

Cheng L., Remote sensing image classification based on optimized support vector machine, TELKOMNIKA Indonesian Journal of Electrical Engineering, 12, 2, pp. 1037-1045, (2014)

[2]

Sai Chandana B., Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image, International Journal of Electrical and Computer Engineering, 4, 6, pp. 923-930, (2014)

[3]

Zarnegarnia Y., Majd H.A., Tavirani M.R., Khaier N., Maboodi A.A.K., Application of fuzzy clustering in analysis of included proteins in esophagus, stomach and colon cancers based on similarity of Gene Ontology annotation, Koomesh, 12, 1, pp. 14-22, (2010)

[4]

Beliakov G., James S., Li G., Learing choquet-integral-based matrices for semi supervised clustering, IEEE Transactions on Fuzzy Systems, 19, 3, pp. 562-574, (2011)

[5]

Fan J., Li J., A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm, Applied Mathematics, 5, 8, pp. 1275-1283, (2014)

[6]

Ze-xuan J., Qiang C., Quan-sen S., De-shen X., Image Segmentation with Anisotropic Weighted Fuzzy C-Means Clustering, Journal of Computer Aided Design & Computer Graphics, 21, 10, pp. 1451-1459, (2009)

[7]

Xiaopeng W., Chongyang H., Yangyu F., Watershed Segmentation Based on Morphological Scale-Space and Gradient Modification, Journal of Electronics & Information Technology, 28, 3, pp. 485-489, (2006)

[8]

Li G., Shuyan Y., Jie X., Et al., A New Marker-Based Watershed Algorithm, Acta Electronica Sinica, 34, 11, pp. 2018-2023, (2006)

[9]

Basran Parminder S., Andrew R., Derek W., CT image artifacts from brachytherapy seed implants: A post processing 3D adaptive median filter, Medical Physics, 38, 2, pp. 712-718, (2011)

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