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

一. 基于图论的方法(Graph-basedalgorithms)：

1.Normalized cuts, 2000.

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

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

Project HomePage: 

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

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

2. Graph-basedsegmentation, 2004.

Pedro Felzenszwalb and DanielHuttenlocher. Efficient graph-basedimage segmentation. InternationalJournal of Computer Vision (IJCV),59(2):167–181, September2004.

Project HomePage: http://cs.brown.edu/~pff/segment/

3. Graph cuts method,2008.

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

Project HomePage: 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 optimizationframework. In European Conference on Computer Vision (ECCV),2010.

Project HomePage: http://www.csd.uwo.ca/~olga/

5. Entropy RateSuperpixel Segmentation, 2011.

Ming-Yu Liu, Tuzel, O.,Ramalingam, S. , Chellappa, R., Entropy Rate SuperpixelSegmentation, 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, InternationalConference 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 onimmersion simulations. IEEE Transactions on Pattern Analalysis andMachine Intelligence, 13(6):583–598, 1991.

2. Mean Shift,2002.

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

3. Quick Shift,2008

A. Vedaldi and S. Soatto. Quickshift and kernel methods for mode seeking. In European Conferenceon Computer Vision (ECCV), 2008.

Project HomePage: 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 PatternAnalysis and Machine Intelligence (PAMI),2009.

Project HomePage: 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 HomePage: http://ivrg.epfl.ch/research/superpixels

6.SEEDS, 2012.

M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, L. VanGool.SEEDS: Superpixels Extracted via Energy-Driven Sampling, ECCV2012.

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