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论文类型：期刊论文

发表时间：2009-11-01

发表刊物：PATTERN RECOGNITION

收录刊物：SCIE、EI、Scopus

卷号：42

期号：11

页面范围：2527-2540

ISSN号：0031-3203

关键字：Clustering; Attribute weights; Center initialization; Fuzzy C-means; Image segmentation

摘要：In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm's problem that the clustering quality is greatly affected by the data distributing and the stochastic initializing the centrals of clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial clustering centers adjacent to the actual final clustering centers can be found by the Proposed single point adjustment method, which Could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved. (C) 2009 Elsevier Ltd, All rights reserved.