Weak boundary preserved superpixel segmentation based on directed graph clustering

Weak boundary preservation is a challenge for superpixel segmentation. Existing methods that measure pixels' similarity based on pairwise distance could not efficiently describe relationship among high-dimensional image data, which plays key roles in extraction for image boundaries. In this paper, we present a new directed graph clustering (DGC)-based superpixel segmentation method via K-nearest-neighbor (K-NN) graph and distance information. It can efficiently deal with segmentation for the weak boundary of complex and irregular object. The basic idea is motivated by following observations: compared with smooth region, image boundary points have much lower pixel density and directed connectivity in local region. Based on K-NN directed graph, we introduce indegree and outdegree to describe above observation, which are the foundation to evaluate pixels' similarity. Then, a patch-based segmentation generates superpixel borders by even overlapping regions. Finally, we solve an integer programming to stitch small noise regions into final superpixels. Experimental results on two benchmarks demonstrate that our method outperforms the state-of-the-arts.