Real Time Superpixel Segmentation by DBSCAN Clustering Algorithm
In this paper, we propose a real time image superpixel segmentation method with 50 frames/s by using the density based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two step framework. In the first clustering stage, the DBSCAN algorithm with color similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state of the art superpixel segmentation methods in terms of both accuracy and efficiency.
IEEE Transactions on Image Processing