Image segmentation is a key step in image analysis and image understanding. Compared with other image segmentation algorithms, mean shift algorithm has some advantages such as simple principle, dispensing with a priori knowledge, capability of dealing with gra y images and complex natural color images, etc. However, the algorithm requires iterative calculations for each pixel in the image, and segmentation computational cost is high for practical tasks. Therefore, a fast mean shift (FMS) method for image segmentation is proposed, in which a small amount of pixels are selected as an initial point for iterative calculation, and other pixels are mergered to the existing classes according to the distance between the pixel and the class centers. As a result, the proposed FMS method reduces the iteration numbers of mean shift algorithm, and boosts the segmentation efficiency. Experimental results show that the proposed FMS method can obtain good segmentation results and higher segmentation efficiency.