A novel real-time superpixel segmentation algorithm

We introduce a new superpixel segmentation algorithm in this paper with a real-time performance that make the practical in the machine vision systems. The algorithm is divided into two steps. First, a simple linear clustering with O(N) complexity is used for efficient initial segmentation. Second, to further optimize the boundary localizations, a region competition skill is first used on the superpixels' edge points and then iterates on the unstable edge points. As only the superpixels' edge points are considered and most edge points become stable quickly, the clustering samples are significantly compressed to speed up the process. Experimental results on the Berkeley BSDS500 dataset show that the segmentation quality of the proposed method is slightly better than the SLIC algorithm, which is a state-of-the-art superpixel segmentation algorithm. In addition, the average speed achieves speedups of about 5X from the original SLIC algorithm, more than 30 frames per second to process 481x321 images in BSDS500.