Boundary-Aware Superpixel Segmentation Based on Minimum Spanning Tree

Abstract

In this paper, we propose a boundary-aware superpixel segmentation method, which could quickly and exactly extract superpixel with a non-iteration framework. The basic idea is to construct a minimum spanning tree (MST) based on structure edge to measure the local similarity among pixels, and then label each pixel as the index with shortest path seeds. Intuitively, we first construct MST on the original pixels with boundary feature to calculate the similarity of adjacent pixels. Then the geodesic distance between pixels can be exactly obtained based on two-round tree recursions. We determinate pixel label as the shortest path seed index. Experimental results on BSD500 segmentation benchmark demonstrate the proposed method obtains best performance compared with seven state-of-the-art methods. Especially for the low density situation, our method can obtain the boundary-aware oversegmentation region.