Optimal multilevel thresholding based on Tsallis entropy using Fibonacci Particle Swarm Optimization for improved Image Segmentation

Image Segmentation based on multilevel thresholding using non-extensive (non-additive) entropy based techniques is challenging, and the optimal choice of thresholds is an effective approach to solve this problem. In this paper, we propose a novel optimization technique based on the Particle Swarm Optimization (PSO) called Fibonacci Particle Swarm Optimization (FPSO) that helps decide the optimal thresholds by maximizing the objective function of Tsallis entropy. The superiority of our proposed method has been demonstrated by comparing the results with some of the contemporary algorithms like Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO), the Standard Particle Swarm Optimization (PSO) and the Golden Ratio Particle Swarm Optimization (GRPSO). The quality of the segmented images has been evaluated using Peak Signal to Noise Ratio (PSNR) and Compression Ratios of the original images and reconstructed images. The results obtained by the proposed method have been found to be significantly better than those obtained by the above mentioned algorithms.

[1]  R. Kayalvizhi,et al.  PSO-Based Tsallis Thresholding Selection Procedure for Image Segmentation , 2010 .

[2]  Xavier P. Burgos-Artizzu,et al.  utomatic segmentation of relevant textures in agricultural images , 2010 .

[3]  R. Kayalvizhi.  Optimum Multilevel Image Thresholding Based on Tsallis Entropy Method with Bacterial Foraging Algorithm , 2010 .

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  Dong Ming,et al.  Infrared gait recognition based on wavelet transform and support vector machine , 2010, Pattern Recognit..

[6]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  YuDong Zhang,et al.  Pattern Recognition via PCNN and Tsallis Entropy , 2008, Sensors.

[8]  Abdul Rahman Ramli,et al.  Review of brain MRI image segmentation methods , 2010, Artificial Intelligence Review.

[9]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[10]  Ellips Masehian,et al.  Particle Swarm Optimization Methods, Taxonomy and Applications , 2009 .

[11]  Yudong Zhang,et al.  Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach , 2011, Entropy.

[12]  K. Manikantan,et al.  Optimal Multilevel Thresholds based on Tsallis Entropy Method using Golden Ratio Particle Swarm Optimization for Improved Image Segmentation , 2012 .

[13]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[15]  Xiao Zhi Gao,et al.  A Hybrid Particle Swarm Optimization Method , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

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

[17]  Hassan M. Emara,et al.  Clubs-based Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.