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VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations
June 2012 (vol. 34 no. 6)
pp. 1241-1247
Jie Wang, Dept. of Sci. Comput., Florida State Univ., Tallahassee, FL, USA
Xiaoqiang Wang, Dept. of Sci. Comput., Florida State Univ., Tallahassee, FL, USA
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.

[1] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC Superpixels," Technical Report 149300, EPFL, June 2010.
[2] W. Burger and M. Burge, Principles of Digital Image Processing. Springer, 2009.
[3] D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach toward Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[4] C. Couprie, L. Grady, L. Najman, and H. Talbot, "Power Watersheds: A New Image Segmentation Framework Extending Graph Cuts, Random Walker and Optimal Spanning Forest," Proc. 12th IEEE Int'l Conf. Computer Vision, pp. 731-738, 2009.
[5] T. Cour, F. Benezit, and J. Shi, "Spectral Segmentation with Multiscale Graph Decomposition," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 1124-1131, 2005.
[6] Q. Du, V. Faber, and M. Gunzburger, "Centroidal Voronoi Tessellations: Applications and Algorithms," SIAM Rev., vol. 41, pp. 637-676, 1999.
[7] Q. Du, M. Gunzburger, and L. Ju, "Constrained Centroidal Voronoi Tessellations on General Surfaces," SIAM J. Scientific Computing, vol. 24, pp. 1488-1506, 2003.
[8] Q. Du, M. Gunzburger, L. Ju, and X. Wang, "Voronoi Tessellation Algorithms for Image Compression and Segmentation," J. Math. Imaging and Vision, vol. 24, pp. 177-194, 2006.
[9] P. Felzenszwalb and D. Huttenlocher, "Efficient Graph-Based Image Segmentation," Int'l J. Computer Vision, vol. 59, pp. 167-181, 2004.
[10] R. Gonzalez and R. Woods, Digital Image Processing. Prentice Hall, 2007.
[11] A. Hausner, "Simulating Decorative Mosaics," Proc. 28th Ann. Conf. Computer Graphics and Interactive Techniques, pp. 573-580, 2001.
[12] L. Ju, "Conforming Centroidal Voronoi Delaunay Triangulation For Quality Mesh Generation," Int'l J. Numerical Analysis and Modeling, vol. 4, pp. 531-547, 2007.
[13] L. Ju, Q. Du, and M. Gunzburger, "Probabilistic Methods for Centroidal Voronoi Tessellations and Their Parallel Implementations," Parallel Computing, vol. 28, pp. 1477-1500, 2002.
[14] T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, "An Efficient k-Means Clustering Algorithm: Analysis and Implementation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.
[15] A. Levinshtein, A. Stere, K.N. Kutulakos, D.J. Fleet, S.J. Dickinson, and K. Siddiqi, "TurboPixels: Fast Superpixels Using Geometric Flows," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2290-2297, Dec. 2009.
[16] C. Li, C. Kao, J. Gore, and Z. Ding, "Minimization of Region-Scalable Fitting Energy for Image Segmentation," IEEE Trans. Image Processing, vol. 17, no. 10, pp. 1940-1949, Oct. 2008.
[17] S. Lloyd, "Least Squares Quantization in PCM," IEEE Trans. Information Theory, vol. 28, no. 2, pp. 129-137, Mar. 1982.
[18] D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics," Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 416-423, 2001.
[19] A. Moore, S. Prince, and J. Warrel, "Lattice Cut—Constructing Superpixels Using Layer Constraints," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2117-2124, 2010.
[20] X. Ren and J. Malik, "Learning a Classification Model for Segmentation," Proc. IEEE Ninth Int'l Conf. Computer Vision, vol. 1, pp. 10-17, 2003.
[21] E. Sharon, A. Brandt, and R. Basri, "Fast Multiscale Image Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 70-77, 2000.
[22] J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 731-737, 1997.
[23] J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[24] A. Vedaldi and S. Soatto, "Quick Shift and Kernel Methods for Mode Seeking," Proc. European Conf. Computer Vision, pp. 705-718, 2008.
[25] O. Veksler, Y. Boykov, and P. Mehrani, "Superpixels and Supervoxels in an Energy Optimization Framework," Proc. European Conf. Computer Vision, pp. 211-224, 2010.
[26] L. Vincent and P. Soille, "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, June 1991.
[27] J. Wang, L. Ju, and X. Wang, "An Edge-Weighted Centroidal Voronoi Tessellation Model For Image Segmentation," IEEE Trans. Image Processing, vol. 18, no. 8, pp. 1844-1858, Aug. 2009.
[28] J. Wang, L. Ju, and X. Wang, "Edge-Weighted Centroidal Voronoi Tessellations," Numerical Math.: Theory, Methods and Applications, vol. 3, pp. 223-244, 2010.
[29] J. Wang, L. Ju, and X. Wang, "Image Segmentation Using Local Variation and Edge-Weighted Centroidal Voronoi Tessellations," IEEE Trans. Image Processing, vol. 20, no. 11, pp. 3242-3256, Nov. 2011.
[30] S. Yu and J. Shi, "Multiclass Spectral Clustering," Proc. Ninth IEEE Int'l Conf. Computer Vision, vol. 1, pp. 313-319, 2003.

Index Terms:
image segmentation,computational complexity,computational geometry,image resolution,complexity analysis,VCells,superpixels,EWCVT,edge-weighted centroidal voronoi tessellations-based algorithm,image oversegmentation,local image boundaries,image undersegmentation,O(K&#x221A;n<;sub>;c<;/sub>;&#x00B7;N),Image segmentation,Clustering algorithms,Algorithm design and analysis,Partitioning algorithms,Shape,Image color analysis,Computational efficiency,clustering.,Superpixels,k-means,centroidal Voronoi tessellations,image segmentation,image labeling
Citation:
Jie Wang, Xiaoqiang Wang, "VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 6, pp. 1241-1247, June 2012, doi:10.1109/TPAMI.2012.47
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