Intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation

In order to discover the detailed information contained in the infrared image, this paper proposes an intuitionistic fuzzy entropy clustering algorithm for image segmentation. Because of the blurred characteristic of the infrared image, the intuitionistic fuzzy sets are selected for infrared image segmentation. The object function of the intuitionistic fuzzy entropy clustering algorithm is constructed by the intuitionistic fuzzy distance and the intuitionistic fuzzy entropy based on the regularization technique. The condition of the ntuitionistic fuzzy entropy clustering algorithm is researched. The Lagrange multiplier method is employed to calculate membership functions and the centroids. An iterative algorithm is deduced to calculate Lagrange multiplier coefficient and membership. Finally, experimental results demonstrate the ability of the intuitionistic fuzzy entropy clustering algorithm for infrared image segmentation.

[1]  I. Vlachos,et al.  Towards Intuitionistic Fuzzy Image Processing , 2005 .

[2]  Jie Tian,et al.  ICM method for multi-level thresholding using maximum entropy criterion , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[3]  William A. Yasnoff,et al.  Error measures for scene segmentation , 1977, Pattern Recognit..

[4]  Tamalika Chaira,et al.  Intuitionistic Fuzzy Segmentation of Medical Images , 2010, IEEE Transactions on Biomedical Engineering.

[5]  Janusz Kacprzyk,et al.  Distances between intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..

[6]  Guo Si-zong.  Entropy of intuitionistic fuzzy sets and its general forms , 2011 .

[7]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..

[8]  Yuan Yan Tang,et al.  Nonlinear shape restoration of distorted images with coons transformation , 1996, Pattern Recognit..

[9]  K. Atanassov.  More on intuitionistic fuzzy sets , 1989 .

[10]  Y. J. Zhang,et al.  A survey on evaluation methods for image segmentation , 1996, Pattern Recognit..

[11]  Tamalika Chaira,et al.  A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images , 2011, Appl. Soft Comput..

[12]  Humberto Bustince,et al.  A-IFSs Entropy Based Image Multi-thresholding , 2010, IEA/AIE.

[13]  Nikos Pelekis,et al.  Intuitionistic Fuzzy Clustering with Applications in Computer Vision , 2008, ACIVS.

[14]  Ioannis K. Vlachos,et al.  Intuitionistic Fuzzy Histograms of an Image , 2007, IFSA.

[15]  Chen Qi.  On clustering approach to intuitionistic fuzzy sets , 2007 .

[16]  Anjana Gosain,et al.  Novel Intuitionistic Fuzzy C-Means Clustering for Li nearly and Nonlinearly Separable Data , 2012 .

[17]  Humberto Bustince,et al.  Intuitionistic fuzzy generators Application to intuitionistic fuzzy complementation , 2000, Fuzzy Sets Syst..

[18]  Zhang Yin.  Fuzzy Clustering Analysis for Foundry Sands , 2001 .

[19]  Ioannis K. Vlachos,et al.  Intuitionistic fuzzy information - Applications to pattern recognition , 2007, Pattern Recognit. Lett..

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  Zeshui Xu,et al.  Intuitionistic fuzzy C-means clustering algorithms , 2010 .

[22]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .