.png){kind=icon}
返回
Improved artificial bee colony algorithm and its application in image threshold segmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-021-11644-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image segmentation is a key problem in the field of computer vision, especially in these fields, such as image processing, analysis and understanding. The key of the problem to be solved is how to obtain reasonable threshold according to the different types of images. Based on these, an improved artificial bee colony algorithm based on Tent mapping in chaos theory is proposed and applied to image threshold segmentation. Firstly, a complementary encoding scheme for the artificial bee colony algorithm is constructed from on the Tent map in chaos theory. According to the characteristics between the current solution and the optimal solution, a fixed orientation updating method is proposed in the colony algorithm updating strategy. The difference between 1 and [0, 1] is still in the range of [0, 1], the way adjust the local optimal solution by the complementary properties. All these construct an Improved Artificial Bee Colony based on Tent Mapping (IABCTM) algorithm. Secondly, according to the characteristics of the algorithm, the algorithm is convergent with probability 1. Finally, the improved algorithm is applied to image threshold segmentation. Through the comparison of multiple images, multiple performance parameters and multiple algorithms, the improved algorithm is proved to have a strong ability to obtain the optimal solution and a good convergence performance.
Keyword:
Tent mapping
Update strategies
Image threshold
Segmentation
Bee colony algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
3D mesh simplification with feature preservation based on Whale Optimization Algorithm and Differential Evolution基于鲸鱼优化算法和差分进化的特征保留三维网格简化
.png){kind=icon}
PREAI
分享收藏
分享收藏
查看更多内容