【摘要】 信息化时代,图像分割技术在医学、国土及交通等诸多领域得到广泛应用。K-means算法具有简单易实现的特点,是常用的图像分割算法,但它存在对初始值选取敏感等缺点。麻雀搜索算法是新提出一种群体智能算法,该算法在搜索进度、收敛速度和稳定性等方面性能优越。该文章利用麻雀搜索算法优化传统K-means算法的初始值,提出SSK-means算法。实验结果表明,对于给出的四组图像,SSK-means算法分割结果均优于传统K-means算法的分割结果。更多还原

【Abstract】 In the information age, image segmentation has been widely used in many fields, such as medicine, land and transportation. K-means algorithm is simple and easy to implement. It is a commonly used image segmentation algorithm, but it also has some shortcomings, such as sensitivity to the selection of initial values. Sparrow search algorithm is a new swarm intelligence algorithm, which has excellent performance in search progress, convergence speed and stability. This paper uses sparrow search algorithm to optimize the initial value of traditional K-means algorithm, and proposes SSK-means algorithm. The experimental results show that for the given six images, the segmentation results of SSK-means algorithm are better than those of traditional K-means algorithm.更多还原