常君杰,李东兴,钟欣,杜文汉,王倩楠.改进乌鸦算法优化多阈值图像分割[J].包装工程,2021,42(11):238-246.
CHANG Jun-jie,LI Dong-xing,ZHONG Xin,DU Wen-han,WANG Qian-nan.Image Segmentation of Multilevel Threshold Based on Improved Crow Search Algorithm[J].Packaging Engineering,2021,42(11):238-246. |
| 改进乌鸦算法优化多阈值图像分割 |
| Image Segmentation of Multilevel Threshold Based on Improved Crow Search Algorithm |
| 投稿时间：2020-09-27 |
| DOI：10.19554/j.cnki.1001-3563.2021.11.035 |
| 中文关键词: 多阈值图像分割 乌鸦搜索算法 精英分享策略 Levy飞行 |
| 英文关键词: multilevel threshold image segmentation crow search algorithm elite sharing strategy levy flight |
| 基金项目:国家自然科学基金(51705296) |
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| 中文摘要: |
| 目的 针对传统乌鸦算法随机搜索的盲目性和易陷入局部最优的缺点，提出一种改进乌鸦算法，用于多阈值图像分割。方法 采用精英分享策略，弥补乌鸦位置更新的盲目性;引入Levy飞行机制，避免算法陷入局部最优;随迭代次数调整变尺度系数，限制搜索步长，加快算法收敛;以Kapur熵为适应函数，利用改进乌鸦算法对不同类型图像进行多阈值分割，并与传统乌鸦、布谷鸟等4种算法的分割结果进行对比分析。结果 改进乌鸦算法对Lena，Flower，Fruits和Boat图分割后的结构相似性分别为0.7703，0.7761，0.7276和0.7921;标准偏差分别为0.0295，0.0385，0.0344和0.0173，实验数据表明，改进算法较其他算法有着更好的分割效果。结论 文中算法有效地改进了传统乌鸦算法的盲目性和易陷入局部最优的缺点，能够准确地分割复杂图像，在多阈值图像分割领域具有一定的参考价值。 |
| 英文摘要: |
| To solve the problems existing in the random search process of the traditional crow algorithm, such as the blindness and tendency to fall into local optimum, an improved crow search algorithm was proposed for multi-threshold image segmentation. The elite sharing strategy was adopted to make up for the blindness of the crow's position when updated. To avoid falling into local optimum, the Levy flight mechanism was introduced. Then the scale coefficients were adaptively adjusted with the number of iterations, which made the search step size of the improved algorithm limited and accelerated the convergence of the algorithm. Kapur entropy was selected as the adaptation function, and the improved crow algorithm was used to perform multi-threshold segmentation on the different types of images in the end, and the results of the algorithm in this article were compared with the segmentation results of the four algorithms such as the traditional crow algorithm, cuckoo algorithm. Lena, Flower, Fruits and Boat were segmented, and the structural similarity of improved crow search algorithm was 0.7703, 0.7761, 0.7276, and 0.7921; the standard deviations were 0.0295, 0.0385, 0.0344, and 0.0173. Experimental data shows that the improved algorithm had better segmentation than other algorithms. The algorithm in this paper effectively improves the blindness and the shortcomings of being easy to fall into the local optimum about the traditional crow algorithm. It can accurately segment complex images, which has certain reference values in the field of multi-threshold image segmentation. |
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