Convex hierarchical segmentation model for images with multi-component
Weibin Li, Xian Yi, Yanxia Du, Fan Zhao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Focus on the multi-component image segmentation issue, a hierarchical model is proposed in this paper. The idea is to do segmentation iteratively. The (k+1)-th implementation is carried out not on the whole image domain but on the subimage which is detected as the objects region at the k-th segmentation. In order to achieve this purpose of selective segmentation, a region characteristic function which takes 1 for pixel in the given region and 0 otherwise is introduced, and a novel energy function is proposed based on it. The proposed energy function is convex, thus it can easily apply the fast minimization algorithm and obtain the global minima. In this paper, the well-known split Bregman method is used to minimize the proposed energy function. Experiments demonstrate that the proposed model is able to deal with multicomponent images. And comparisons show that the model is more accurate and efficient.