目的 针对樽海鞘群算法寻优精度低、易陷入到局部最优，以及K-means算法进行图像分割容易被初始聚类中心干扰等缺点，提出改进樽海鞘群优化K-means算法的图像分割。方法 首先利用Circle映射来对樽海鞘种群进行初始化;其次引入莱维飞行到领导者和追随者位置更新公式中，使得樽海鞘种群的多样性得到提高，克服算法陷入到局部最优。最后，对改进樽海鞘群算法先采用8个基准函数进行性能测试;再将改进樽海鞘群算法优化K-means进行图像分割。结果 改进算法在寻优精度、稳定性、收敛速度以及跳出局部最优的本领得到了提高。同时，改进樽海鞘群优化K-means算法进行图像分割，有效地提高了图像分割质量。结论 改进算法改善了原始樽海鞘群算法的寻优精度低、易陷入到局部最优的缺点，很好地优化了K-means算法对图像进行准确分割，在图像分割领域具有一定的参考意义。

In view of the disadvantages of salp swarm optimization algorithm, such as low optimization accuracy, easy to fall into local optimum, and K-means algorithm for image segmentation easily disturbed by the initial cluster center, an improved salp swarm optimization K-means algorithm was proposed for image segmentation. Firstly, circle mapping was used to initialize the salp population. Secondly, Levy flight was introduced into the leader and follower position updating formula to improve the diversity of salp population and overcome the algorithm falling into local optimum. Finally, eight benchmark functions were used to test the performance of the improved salp population swarm algorithm. Then, the improved salp swarm algorithm is optimized with K-means for image segmentation. The improved algorithm improves the searching accuracy, stability, convergence speed and the ability to jump out of local optimum. At the same time, the K-means algorithm was optimized by improving salp swarm algorithm to improve image segmentation quality effectively. The improved algorithm improves the disadvantages of the original salp swarm algorithm, such as low optimization accuracy and easy to fall into the local optimum, and can effectively optimize the K-means algorithm for accurate image segmentation, which has a certain reference significance in the field of image segmentation.