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Open AccessArticle

2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

1
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(4), 239; https://doi.org/10.3390/e20040239
Received: 11 March 2018 / Revised: 27 March 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
(This article belongs to the Special Issue Research Frontier in Chaos Theory and Complex Networks)
Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and differential evolution algorithm (DE) are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA). The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation. View Full-Text
Keywords: 2D Tsallis entropy; image segmentation; Bat algorithm; chaotic process; Levy flight 2D Tsallis entropy; image segmentation; Bat algorithm; chaotic process; Levy flight
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Ye, Z.; Yang, J.; Wang, M.; Zong, X.; Yan, L.; Liu, W. 2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm. Entropy 2018, 20, 239.

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