A Fuzzy Entropy Based Multi-Level Image Thresholding Using Differential Evolution

This paper presents a multi-level image thresholding approach based on fuzzy partition of the image histogram and entropy theory. Here a fuzzy entropy based approach is adopted in context to the multi-level image segmentation scenario. This entropy measure is then optimized to obtain the thresholds of the image. In order to solve the optimization problem, a meta-heuristic, Differential Evolution (DE) is used, which leads to a faster and accurate convergence towards the optima. The performance of DE is also measured with respect to some popular global optimization techniques like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs).The outcomes are compared with Shannon entropy, both visually and statistically in order to establish the perceptible difference in image.

[1]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

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

[3]  Hong Yan,et al.  A technique of three-level thresholding based on probability partition and fuzzy 3-partition , 2001, IEEE Trans. Fuzzy Syst..

[4]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[5]  James David,et al.  Remote Control , 2008 .

[6]  Isabelle Bloch,et al.  Fuzzy spatial relationships for image processing and interpretation: a review , 2005, Image Vis. Comput..

[7]  Chun-hung Li,et al.  Minimum cross entropy thresholding , 1993, Pattern Recognit..

[8]  Michael A. Arbib,et al.  Computational Techniques in the Visual Segmentation of Static Scenes. , 1977 .

[9]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[10]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[11]  Zhou Wang,et al.  Complex Wavelet Structural Similarity: A New Image Similarity Index , 2009, IEEE Transactions on Image Processing.

[12]  Swagatam Das,et al.  Multilevel Image Thresholding Based on Tsallis Entropy and Differential Evolution , 2012, SEMCCO.

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  Andrew K. C. Wong,et al.  A gray-level threshold selection method based on maximum entropy principle , 1989, IEEE Trans. Syst. Man Cybern..

[15]  Swagatam Das,et al.  A Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding , 2011, SEMCCO.

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

[17]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[18]  Zhongke Shi,et al.  The strongest schema learning GA and its application to multilevel thresholding , 2008, Image Vis. Comput..

[19]  Nikhil R. Pal,et al.  On minimum cross-entropy thresholding , 1996, Pattern Recognit..

[20]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[21]  Wenbing Tao,et al.  Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm , 2003, Pattern Recognit. Lett..

[22]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[23]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..