超像素分割技术发展情况梳理(Superpixel Segmentation）

Sason@CSDN

当前更新日期：2013.06.10

一. 基于图论的方法(Graph-based algorithms)：

1. Normalized cuts, 2000.

Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22(8):888–905,  2000.

T. Cour, F. Benezit, and J. Shi. Spectral segmentation with multiscale graph decomposition. In IEEE Computer Vision and Pattern Recognition (CVPR) 2005, 2005.

Project Home Page:

http://www.cis.upenn.edu/~jshi/software/

http://www.timotheecour.com/software/ncut/ncut.html

2. Graph-based segmentation, 2004.

Pedro Felzenszwalb and Daniel Huttenlocher. Efficient graph-basedimage segmentation. International Journal of Computer Vision (IJCV),59(2):167–181, September 2004.

Project Home Page: http://cs.brown.edu/~pff/segment/

3. Graph cuts method, 2008.

Alastair Moore, Simon Prince, Jonathan Warrell, Umar Mohammed, andGraham Jones. Superpixel Lattices. IEEE Computer Vision and PatternRecognition (CVPR), 2008.

Project Home Page: http://www.cs.sfu.ca/~mori/research/superpixels

4. GCa10 and GCb10, 2010.

O. Veksler, Y. Boykov, and P. Mehrani. Superpixels and supervoxels in an energy optimization framework. In European Conference on Computer Vision (ECCV), 2010.

Project Home Page: http://www.csd.uwo.ca/~olga/

5. Entropy Rate Superpixel Segmentation, 2011.

Ming-Yu Liu, Tuzel, O., Ramalingam, S. , Chellappa, R., Entropy Rate Superpixel Segmentation, CVPR,2011.

Project Home Page:http://www.umiacs.umd.edu/~mingyliu

6. Superpixels via Pseudo-Boolean Optimization, 2011.

Yuhang Zhang, Richard Hartley, John Mashford and Stewart Burn, Superpixels via Pseudo-Boolean Optimization, International Conference on Computer Vision (ICCV), 2011.

http://yuhang.rsise.anu.edu.au/yuhang/misc.html

二. 基于梯度下降的方法(Gradient-ascent-based algorithms)：

1. Watershed,1991.

Luc Vincent and Pierre Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analalysis and Machine Intelligence, 13(6):583–598, 1991.

2. Mean Shift, 2002.

D. Comaniciu and P. Meer. Mean shift: a robust approach toward featurespace analysis. IEEE Transactions on Pattern Analysis and MachineIntelligence, 24(5):603–619, May 2002.

3. Quick Shift, 2008

A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In European Conference on Computer Vision (ECCV), 2008.

Project Home Page: http://www.vlfeat.org/download.html

4. Turbopixel, 2009.

A. Levinshtein, A. Stere, K. Kutulakos, D. Fleet, S. Dickinson, and K. Siddiqi. Turbopixels: Fast superpixels using geometric flows. IEEETransactions on Pattern Analysis and Machine Intelligence (PAMI),2009.

Project Home Page: http://www.cs.toronto.edu/~babalex/

5. SLIC, 2010.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk , SLIC Superpixels, 2010.

Project Home Page: http://ivrg.epfl.ch/research/superpixels

6.SEEDS, 2012.

M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. Van Gool.SEEDS: Superpixels Extracted via Energy-Driven Sampling, ECCV 2012.

Project Home Page:http://www.vision.ee.ethz.ch/~boxavier/seeds/

自然图像抠图/视频抠像技术发展情况梳理（image matting, alpha matting, video matting）--计算机视觉专题1

http://blog.csdn.net/anshan1984/article/details/8581225

图像/视觉显著性检测技术发展情况梳理(Saliency Detection、Visual Attention）--计算机视觉专题2
http://blog.csdn.net/anshan1984/article/details/8657176

超像素分割技术发展情况梳理(Superpixel Segmentation）--计算机视觉专题3
http://blog.csdn.net/anshan1984/article/details/8918167

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