Image Segmentation Algorithm Based on Particle Swarm Optimization with K-means Optimization

In order to achieve accurate and efficient image segmentation, this paper proposes a Particle swarm optimization (PSO) algorithm and k-means aggregation hybrid image segmentation algorithm, which aims to solve the problem of selecting the initial center of k-means clustering and improve the disadvantages of easily falling into local optimal. Firstly, the image is de-noised and the processed color image is converted into HSV space to improve the color quality. Then, global search ability is improved by adjusting the inertia weight and learning factor in the particle swarm optimization algorithm. Finally, the local search is performed by switching to the k-means algorithm according to the particle fitness, so that the clustering center is constantly updated to achieve rapid convergence. Segmentation results show that the proposed algorithm can segment images accurately, and have a higher segmentation efficiency than k-means algorithm, traditional particle swarm optimization and k-means algorithm (PSOK).