基于PSO与K-均值聚类算法优化结合的图像分割方法

为了提高图像分割的质量和效率，同时，针对粒子群优化算法（Particle Swarm Optimization, PSO）容易陷于局部最优和K-均值算法对初始聚类中心敏感的问题，本文将PSO和K-均值算法相结合，提出一种通过调整惯性权重和学习因子的优化算法。首先，对图像进行去噪预处理，并将处理后的颜色图像转换到HSV空间，以提高色彩质量。然后，改进粒子群算法中的惯性权重和学习因子公式及参数，避免陷入局部最优。最后，根据粒子的适应度切换到K-均值算法执行局部搜索，使聚类中心不断更新实现快速收敛。实验结果表明，在图像分割的过程中，改进的算法具有全局搜索能力强的优点，能够实现更快的收敛速度和更高的分割精度。

Image Segmentation Method Based on Optimization of PSO Algorithm #br# and K-means Clustering Algorithm

In order to improve the quality and efficiency of image segmentation, and considering the weakness that particle swarm optimization (PSO) algorithm is easy to fall into local optimum and that K-means algorithm is sensitive to initial clustering center, combining PSO with K-means algorithm, an optimization algorithm is proposed through the adjustment of inertia weight and learning factor. First, the image is denoised and pre-processed, and the processed color image is converted to the HSV space to improve the color quality. Then, the formula and parameters of the inertia weight and learning factor in the particle swarm optimization algorithm are improved to avoid falling into local optimum. Finally, according to the fitness of the particles, the K-means algorithm is switched to perform a local search, so that the cluster center is continuously updated to achieve fast convergence. In the process of image segmentation, the experimental results show that this improved algorithm has strong ability in global search and it performs well in faster convergence speed and higher segmentation accuracy.