Abstract: 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.

Key words: image segmentation, PSO, K-means, inertia weight, learning factor