Multiscale Superpixels and Supervoxels Based on Hierarchical Edge-Weighted Centroidal Voronoi Tessellation

作者：Zhou Youjie^*; Ju Lili; Wang Song

来源：IEEE Transactions on Image Processing, 2015, 24(11): 3834-3845.

DOI：10.1109/TIP.2015.2449552

• Superpixel
• supervoxel
• image segmentation
• hierarchical image segmentation
• edge-weighted centroidal Voronoi tessellation

Superpixels and supervoxels play an important role in many computer vision applications, such as image segmentation, object recognition, and video analysis. In this paper, we propose a new hierarchical edge-weighted centroidal Voronoi tessellation (HEWCVT) method for generating superpixels/supervoxels in multiple scales. In this method, we model the problem as a multilevel clustering process: superpixels/supervoxels in one level are clustered to obtain larger size superpixels/supervoxels in the next level. In the finest scale, the initial clustering is directly conducted on pixels/voxels. The clustering energy involves both color similarities and boundary smoothness of superpixels/supervoxels. The resulting superpixels/supervoxels can be easily represented by a hierarchical tree which describes the nesting relation of superpixels/supervoxels across different scales. We first investigate the performance of obtained superpixels/supervoxels under different parameter settings, then we evaluate and compare the proposed method with several state-of-the-art superpixel/supervoxel methods on standard image and video data sets. Both quantitative and qualitative results show that the proposed HEWCVT method achieves superior or comparable performances with other methods.

• 出版日期2015-11