Supervoxel segmentation using spatio-temporal lazy random walks

Superpixel segmentation has been proved its effectiveness as a preprocessing step for many applications. Similarly, the over-segmentation with temporal consistency of a video can be useful for quite a few researches. In this paper, a novel supervoxel segmentation algorithm based on lazy random walk is proposed. In the proposed framework, a superpixel segmentation is first applied to each frame and the produced superpixels are called special-pixels. Then, we propose a generalized spatio-temporal adjacency matrix, which keeps the information of special-pixels with neighboring relationship, for the generation of supervoxels using lazy random walk approach. In addition, the center relocation and the splitting strategy of supervoxels are proposed to improve the quality of the supervoxels.