Tsallis Entropy Threshold Image Segmentation Algorithm Based on Improved Honey Bee Mating Algorithm

MA Qiao-mei; LI Jing-jing; KANG Jun

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Tsallis Entropy Threshold Image Segmentation Algorithm Based on Improved Honey Bee Mating Algorithm

更新时间：2026-09-04

- Tsallis Entropy Threshold Image Segmentation Algorithm Based on Improved Honey Bee Mating Algorithm
- Vol. 35, Issue 4, Pages: 32-36(2018)
- 作者机构：
  
  中北大学机构软件学院,山西,太原,030051
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
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马巧梅, 李婧婧, 康珺. 基于改进蜂群繁殖算法的Tsallis熵阈值图像分割[J]. 微电子学与计算机, 2018, 35(4): 32-36.

MA Qiao-mei, LI Jing-jing, KANG Jun. Tsallis Entropy Threshold Image Segmentation Algorithm Based on Improved Honey Bee Mating Algorithm[J]. 2018, 35(4): 32-36.
马巧梅, 李婧婧, 康珺. 基于改进蜂群繁殖算法的Tsallis熵阈值图像分割[J]. 微电子学与计算机, 2018, 35(4): 32-36. DOI：

MA Qiao-mei, LI Jing-jing, KANG Jun. Tsallis Entropy Threshold Image Segmentation Algorithm Based on Improved Honey Bee Mating Algorithm[J]. 2018, 35(4): 32-36. DOI：

摘要

针对传统蜂群繁殖算法存在的不足

提出了一种改进蜂群繁殖算法.首先

将传统蜂群繁殖算法中自定义的繁殖概率和突变概率改进为自适应的

以提高算法的快速收敛性和全局寻优能力; 其次

引入精英保留策略

以缩短寻优时间; 最后

结合Tsallis熵的非广延性

采用Tsallis熵作为阈值图像分割的适应函数

利用改进后的蜂群繁殖算法实现多阈值图像分割

避免图像分割过程中非可加信息被忽略

进而提高图像的分割精度.为了验证所提出算法的可行性

对该算法进行仿真

并与多种算法进行对比研究.实验结果表明

该算法在寻优能力、快速收敛能力、图像分割精度、图像分割速度等方面都有进一步的提高.

Abstract

Aiming at the shortcomings of the traditional honey bee mating algorithm

an improved honey bee mating algorithm was proposed. Firstly

the custom breeding probability and mutation probability in the traditional honey bee mating algorithm were improved to be adaptive to improve the fast convergence and global optimization ability of the algorithm; Secondly

the elite retention strategy was introduced to shorten the optimization time; Finally

with the non-extensibility of Tsallis entropy

using the Tsallis entropy as the fitness function of threshold image segmentation

and the improved honey bee mating algorithm was used to achieve multi-threshold image segmentation to avoid the non-add-on information of the image segmentation process being ignored

thereby improving the accuracy of image segmentation. In order to verify the feasibility of the proposed algorithm

the algorithm was simulated

and compared with some algorithms. The experimental results showed that the algorithm had further improved the optimization ability

fast convergence ability

image segmentation accuracy

image segmentation speed

and so on.

关键词

Keywords

references

Jiang Y, Tsai P, Yeh W C, et al. A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem[J]. Applied Soft Computing, 2017, 52 (C) : 1181-1190. DOI: 1181

Jiang Y, Yeh W C, Hao Z, et al. A cooperative honey bee mating algorithm and its application in multi-threshold image segmentation[J]. Information Sciences, 2016, 369(1): 171-183. DOI: 171

Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transactions on Systems Man % Cybernetics, 1994, 24(4): 656-667. DOI: 656

Abbass H A. MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach[C]// Evolutionary Computation, 2001. Proceedings of the 2001 Congress on. [s. l. ], IEEE, 2015: 207-214.

Jiang J, Jiang T, Zhai S. A novel recognition system for human activity based on wavelet packet and support vector machine optimized by improved adaptive genetic algorithm[J]. Physical Communication, 2014, 13(c): 211-220. DOI: 211

Ren Z W, San Y. Improved adaptive genetic algorithm and its application research in parameter identification[J]. Journal of System Simulation, 2006, 18(1): 41-40. DOI: 41

邸秋艳. 基于Tsallis熵的阈值图像分割方法研究[D]. 河北, 秦皇岛: 燕山大学, 2009: 19-21.

Horng M H. A multilevel image thresholding using the honey bee mating optimization[J]. Applied Mathematics % Computation, 2010, 215(9): 3302-3310. DOI: 3302

Marinakis Y, Marinaki M. A hybrid honey bees mating optimization algorithm for the probabilistic traveling salesman problem[C]// Eleventh Conference on Congress on Evolutionary Computation. Trondheim, Norway, IEEE Press, 2009: 1762 - 1769.

肖林云, 陈秀宏, 林喜兰. 邻域信息熵的核模糊C均值聚类图像分割算法[J]. 微电子学与计算机, 2016, 33(11): 166-170. DOI: 166

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