Graph segmentation revisited: Detailed analysis and density learning based implementation

Zhiding Yu, Oscar C. Au, Ketan Tang, Jiali Li, Lingfeng Xu, Xingyu Zhang

Abstract: In this paper we give a step-by-step detailed analysis on the performance of shortest spanning tree (SST) and its revised version, recursive SST (RSST). We further propose a novel segmentation scheme based on recursive SST in the warped domain produced by density estimation. The proposed method is robust for variant natural image input and is easy to implement. Experimental results and comparisons with other methods have illustrated the effectiveness and robustness of the proposed method.