Simple and fast image superpixels generation with color and boundary probability

Abstract

As one of the most popular preprocessing steps in computer vision fields, superpixel generation algorithm has been extensively studied in recent years. Researchers have to find a way to produce superpixels with both accuracy and computationally efficiency. Inspired by the real-time superpixel segmentation method using density-based spatial clustering of applications with noise (DBSCAN), we propose a two-stage, non-iterative superpixel segmentation approach. In the first stage, we produce the initial regions. To make the superpixels attach to most object boundaries well, we define an adaptive parameter based on the boundary probability map in the distance measurement. At the same time, we adopt the averaging colors of region to represent the cluster center feature. In the second stage, we merge small regions to produce superpixels. To make them have uniform sizes, we take the initial region size into consideration and define a new distance measurement between the two neighboring regions. In the whole framework, we process all the pixels only once. We test the proposed method on the public data sets. The experimental results show that our proposed algorithm outperforms the most compared approaches with accuracy and has competitive speed with the real-time methods (e.g., DBSCAN).