Title :
VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations
Author :
Wang, Jie ; Wang, Xiaoqiang
Author_Institution :
Dept. of Sci. Comput., Florida State Univ., Tallahassee, FL, USA
fDate :
6/1/2012 12:00:00 AM
Abstract :
VCells, the proposed Edge-Weighted Centroidal Voronoi Tessellations (EWCVTs)-based algorithm, is used to generate superpixels, i.e., an oversegmentation of an image. For a wide range of images, the new algorithm is capable of generating roughly uniform subregions and nicely preserving local image boundaries. The undersegmentation error is effectively limited in a controllable manner. Moreover, VCells is very efficient with core computational cost at O(K√nc·N) in which K, nc, and N are the number of iterations, superpixels, and pixels, respectively. Extensive qualitative discussions are provided, together with the high-quality segmentation results of VCells on a wide range of complex images. The simplicity and efficiency of our model are demonstrated by complexity analysis, time, and accuracy evaluations.
Keywords :
computational complexity; computational geometry; image resolution; image segmentation; EWCVT; O(K√n<;sub>;c<;/sub>;·N); VCells; complexity analysis; edge-weighted centroidal voronoi tessellations-based algorithm; image oversegmentation; image undersegmentation; local image boundaries; superpixels; Algorithm design and analysis; Clustering algorithms; Computational efficiency; Image color analysis; Image segmentation; Partitioning algorithms; Shape; Superpixels; centroidal Voronoi tessellations; clustering.; image labeling; image segmentation; k-means; Algorithms; Image Processing, Computer-Assisted; Models, Statistical; Pattern Recognition, Automated;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2012.47