Superpixel segmentation: An evaluation

Abstract

In recent years, superpixel algorithms have become a standard tool in computer vision and many approaches have been proposed. However, different evaluation methodologies make direct comparison difficult. We address this shortcoming with a thorough and fair comparison of thirteen state-of-the-art superpixel algorithms. To include algorithms utilizing depth information we present results on both the Berkeley Segmentation Dataset [3] and the NYU Depth Dataset [19]. Based on qualitative and quantitative aspects, our work allows to guide algorithm selection by identifying important quality characteristics.