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基于改进混沌蝙蝠算法的二维Tsallis熵图像分割

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

作者信息

Ye Zhiwei, Yang Juan, Wang Mingwei, Zong Xinlu, Yan Lingyu, Liu Wei

机构信息

School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

出版信息

Entropy (Basel). 2018 Mar 30;20(4):239. doi: 10.3390/e20040239.

DOI:10.3390/e20040239
PMID:33265330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7512754/
Abstract

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.

摘要

图像分割是图像分析和计算机视觉中的一个重要步骤。在这个主题中已经提出了许多基于熵的方法；其中，Tsallis熵是性能最佳的方法之一。然而，一维Tsallis熵没有利用邻域内的空间相关信息，结果可能会被噪声破坏。因此，提出了二维Tsallis熵来解决这个问题，并将结果与一维Fisher熵、一维最大熵、一维交叉熵、一维Tsallis熵、模糊熵、二维Fisher熵、二维最大熵和二维交叉熵进行比较。另一方面，由于存在巨大的计算成本，遗传算法（GA）、粒子群优化算法（PSO）、蚁群优化算法（ACO）和差分进化算法（DE）等元启发式算法被用于加速二维Tsallis熵阈值化方法。在本文中，将二维Tsallis熵视为一个约束优化问题，通过使用改进的混沌蝙蝠算法（MCBA）最大化目标函数来获得最优阈值。所提出的算法已经在一些实际图像和红外图像上进行了测试。结果与PSO、GA、ACO和DE的结果进行了比较，表明所提出的方法优于本文中涉及的其他方法，是一种可行且有效的图像分割方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/253c2d215732/entropy-20-00239-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/482c3de646b7/entropy-20-00239-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/965951f0ae70/entropy-20-00239-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/bca92005c090/entropy-20-00239-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/6df01d711d91/entropy-20-00239-g010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/253c2d215732/entropy-20-00239-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/191143793763/entropy-20-00239-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/f082006d38b7/entropy-20-00239-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/6b764142b8b2/entropy-20-00239-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/fe811256f252/entropy-20-00239-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/9fcd35394c27/entropy-20-00239-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/482c3de646b7/entropy-20-00239-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/965951f0ae70/entropy-20-00239-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/bca92005c090/entropy-20-00239-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/6df01d711d91/entropy-20-00239-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/15270f9271a0/entropy-20-00239-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/33aa2c663e6e/entropy-20-00239-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b9d/7512754/253c2d215732/entropy-20-00239-g013.jpg

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