基于Logistic回归麻雀算法的图像分割

针对麻雀搜索算法后期种群多样性减少、易陷入局部最优解等问题，提出一种新的改进麻雀搜索算法。所提算法先引入小孔成像反向学习策略对发现者的位置进行更新，提升寻优位置的多样性；其次受Logistic模型的启发，提出一种新的自适应因子对安全阈值进行动态控制，平衡所提算法的全局搜索与局部开发的能力。通过与其他算法在6个基准函数上进行仿真对比，结果表明：所提算法的收敛精度与速度均优于其他算法。在工程应用上，用所提算法优化K-means聚类算法进行图像分割，峰值信噪比（PSNR）、结构相似性（SSIM）及特征相似性（FSIM）3种度量指标验证了其良好的分割性能。

Image segmentation based on Logistic regression sparrow algorithm

The sparrow search algorithm is improved to address its decrease of population diversity in the later stage and its easy fall into the local optimal solution. The improved algorithm introduces the oppositional learning strategy based small hole imaging to update the discoverer’s position, enhancing the diversity of the optimal position. Then, inspired by the Logistic model, a new adaptive factor is proposed to dynamically control the safety threshold, thus balancing the global search and local development capabilities of the algorithm. Simulations of comparison with other algorithms in six benchmark functions are conducted, and experimental results show higher convergence accuracy and speed of the improved algorithm than those of the other algorithms. In engineering applications, the proposed algorithm optimizes the K-means clustering algorithm for image segmentation with satisfactory segmentation performance in terms of peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).