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

基于改进蜂群繁殖算法的Tsallis熵阈值图像分割

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

  • 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.

     

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