Multiscale Superpixels and Supervoxels Based on Hierarchical Edge-Weighted Centroidal Voronoi Tessellation

Abstract

Super pixels and super voxels play an important role in many computer vision applications, such as image segmentation, object recognition and video analysis. In this paper, we propose a hierarchical edge-weighted centroidal Voronoi tessellation (HEWCVT) method for generating superpixels/supervoxels in multiple scales. In this method we model the problem as a multilevel clustering process: superpixels/supervoxels in one level are clustered to obtain larger size superpixels/supervoxels in the next level. In the finest scale, the initial clustering is directly conducted on pixels/voxels. The clustering energy involves both color/feature similarities and the proposed boundary smoothness of superpixels/supervoxels. The resulting superpixels/supervoxels can be easily represented by a hierarchical tree which describes the nesting relation of superpixels/supervoxels across different scales. We evaluate and compare the proposed method with several state-of-the-art superpixel/supervoxel methods on standard image and video datasets. Both quantitative and qualitative results show that the proposed HEWCVT method achieves superior or comparable performances to other methods.