转自：http://blog.csdn.net/anshan1984/article/details/8918167
最近实验需要用到超像素的一些算法，之前也有看过一下分水岭这个老算法，想着找找近年来的新算法，跟上时代的步伐。。然后找到这个。。虽然截至13年，但也是至今为止影响力较大的一些算法了，这两年的许多文章是基于这些算法改进的。感谢大神整理~
超像素分割(Superpixel Segmentation）技术发展情况梳理
当前更新日期：2013.06.10
一. 基于图论的方法(Graph-based algorithms)：
1. Normalized cuts, 2000.
【超像素分割(Superpixel Segmentation）发展】 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. Ef?cient 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 ef?cient 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 ?ows. 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/
