转自:http://blog.youkuaiyun.com/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.
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
- 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/
- 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
- 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/
- 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
- 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):
- 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.
- 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.
- 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
- 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/
- 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/