Abstract: Considering the problem of superpixel segmentation,this paper propose an iterative algorithm of superpixel segmentation based on Ball-k-means clustering to further improve the edge fit of superpixels.Firstly,the superpixels are regarded as five-dimensional hyperspheres,and the image is evenly segmented to obtain the initial superpixels.Secondly,the neighbor superpixels are searched according to the radius and distance between the centers of adjacent superpixels.Then,using the distances between the superpixels and their neighbor superpixel centers,the superpixels are divided into a stable region and multiple ring active regions.Finally,the pixels in each annular active area are divided into the nearest neighbor superpixel only according to their distance from the center of some neighbor superpixels,so as to realize the superpixel segmentation iteratively.In order to reduce the distance calculation and speed up the convergence,a judgment theorem of the relation between the nearest neighbor superpixels is given,and an adaptive partition updating strategy is designed for the superpixel class labels of pixels.Experimental comparison and analysis on BSD500 data set show that the proposed algorithm has better segmentation effect on different types of images,with higher edge fitting degree,less influence by parameters,and more stable segmentation results.

Key words: Image segmentation, Superpixel, Clustering, Ball cluster