Alpha Matting Evaluation Website
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Image matting evaluation results      Competition:   Low resolution  High resolution  
Error type:  SAD   MSE   Gradient   Connectivity  
Sum of Absolute Differences overall
avg.
small
avg.
large
avg.
user
Troll
(Strongly Transparent)
Input  
Doll
(Strongly Transparent)
Input  
Donkey
(Medium Transparent)
Input  
Elephant
(Medium Transparent)
Input  
Plant
(Little Transparent)
Input  
Pineapple
(Little Transparent)
Input  
Plastic bag
(Highly Transparent)
Input  
Net
(Highly Transparent)
Input  
  rank rank rank rank small large user small large user small large user small large user small large user small large user small large user small large user
Information-flow matting3.13.82.82.910.3 1 11.2 1 12.5 3 5.6 7 7.3 6 7.3 6 3.8 2 4.1 2 3 2 1.4 2 2.3 2 2 2 5.9 9 7.1 3 8.6 1 3.6 2 5.7 2 4.6 2 18.3 3 19.3 3 15.8 2 20.2 4 22.2 3 22.3 5
Deep Matting3.342.43.410.7 2 11.2 2 11 1 4.8 1 5.8 1 5.6 1 2.8 1 2.9 1 2.9 1 1.1 1 1.1 1 2 1 6 12 7.1 2 8.9 3 2.7 1 3.2 1 3.9 1 19.2 5 19.6 5 18.7 7 21.8 9 23.9 6 24.1 12
DCNN Matting4.55.92.64.912 11 14.1 5 14.5 8 5.3 2 6.4 3 6.8 4 3.9 3 4.5 3 3.4 4 1.6 9 2.5 3 2.2 5 6 11 6.9 1 9.1 4 4 4 6 3 5.3 4 19.9 6 19.2 2 19.1 9 19.4 1 20 1 21.2 1
Three-layer graph model8.57.16.911.411.4 9 13.9 4 13 4 5.4 3 5.9 2 7.9 12 4.4 9 5.5 16 3.9 11 1.5 5 3.2 8 3 19 6.1 13 7.6 5 13.2 21 4.8 12 8.8 12 7.3 18 19 4 20.1 6 16.4 4 19.9 2 21.1 2 22 2
ATPM Matting11.514.512.97.114 27 17.8 12 13.4 5 5.5 4 6.4 4 7.3 7 5.4 34 6.4 29 4.3 22 1.7 12 3.3 10 2.3 8 6.8 20 8 8 8.7 2 4.2 5 7.5 6 5.5 5 17.2 1 17.6 1 15.7 1 22.6 13 37.3 33 22.8 7
CSC Matting12.315.88.312.813.6 24 15.6 6 14.5 7 6.2 15 7.5 8 8.1 18 4.6 16 4.8 5 4.2 20 1.8 15 2.7 4 2.5 10 5.5 4 7.3 4 9.7 5 4.6 10 7.6 7 6.9 14 23.7 15 23 14 21 14 26.3 27 27.2 18 25.2 14
LNSP Matting139.412.816.812.2 12 22.5 30 19.5 35 5.6 5 8.1 10 8.8 27 4.6 14 5.9 21 3.6 6 1.5 6 3.5 13 3.1 22 6.2 14 8.1 10 10.7 9 4 3 7.1 4 6.4 10 21.5 9 20.8 7 16.3 3 22.5 12 24.4 7 27.8 22
Graph-based sparse matting13.213.813.812.112.6 17 20.5 22 14.8 12 5.7 9 7.3 5 6.4 3 4.5 12 5.3 12 3.7 7 1.4 4 3.3 11 2.3 7 6.3 16 7.9 6 11.1 10 4.2 6 8.3 11 6.4 9 28.7 31 31.3 32 27.1 28 23.6 15 25.1 11 27.3 21
Patch-based Matting13.38.41516.610.9 4 19 16 15.7 18 6 12 9.5 24 8.3 20 4.3 7 5.2 9 4.2 19 1.6 8 3.2 7 2.6 11 5.2 1 9 16 12.4 14 4.7 11 9.7 19 7 15 21.6 10 21.7 8 24.9 21 23.5 14 28.1 21 25.6 15
KL-Divergence Based Sparse Sampling13.912.913.315.511.6 10 17.5 11 14.7 9 5.6 6 8.5 15 8 14 4.9 27 5.3 10 3.7 8 1.5 7 3.5 12 2.1 3 5.8 6 8.3 11 14.1 25 5.6 22 9.3 17 8 23 24.6 19 27.7 26 28.9 31 20.7 6 22.7 4 23.9 11
TSPS-RV Matting1513.51417.511.3 8 16.4 9 13.7 6 6.1 13 8.1 12 8.6 24 4.5 10 5.4 14 4.1 17 1.4 3 3.3 9 3.5 31 7.9 30 8.9 14 12.4 16 6.2 26 9 15 8.7 28 22.8 13 23.5 16 21.4 15 20.7 5 28.5 23 22.2 3
Iterative Transductive Matting15.616.815.414.813.1 21 17.2 10 15.6 17 5.7 8 8.6 17 7.8 11 5.1 30 5.5 15 3.9 9 1.9 16 5.8 27 2.6 12 6.6 18 8.5 12 13.8 24 5.4 17 10 20 7.4 20 25.5 21 24 17 23.8 19 20.1 3 22.7 5 22.7 6
Comprehensive sampling15.813.415.818.411.2 7 18.5 15 14.8 10 6.5 20 9.5 23 8.9 29 4.5 11 4.9 6 4.1 16 1.7 11 3.1 6 2.3 9 5.4 3 9.8 20 13.4 22 5.5 20 11.5 26 7.4 22 23.9 17 22 10 22.8 16 23.8 18 28 20 28.1 23
SVR Matting16.118.815.613.918.7 39 30.7 41 19.1 32 6.8 26 7.7 9 7.6 10 4.7 22 5 8 3.4 3 1.9 17 4.7 19 2.9 17 5.8 5 8.7 13 10.5 6 4.3 7 8 9 5.6 7 21.2 8 22.1 11 17.1 6 25.6 26 26.1 15 30.6 30
Comprehensive Weighted Color and Texture16.116.116.915.314.6 28 16 7 15.7 19 6.8 25 10 27 7.9 13 4.3 6 5 7 4.1 14 1.7 10 3.5 14 2.2 4 5.4 2 9.9 22 12.8 19 4.3 8 7.4 5 5.2 3 28.3 30 28.1 27 25.4 24 24 20 30.2 26 28.7 26
Sparse coded matting16.619.517.113.113.7 25 25.8 37 14.8 13 6.4 17 8.2 13 6.2 2 4.7 17 5.4 13 4 12 1.8 14 3.1 5 2.3 6 5.9 8 8 9 10.6 7 4.5 9 8 8 5.5 6 30.3 33 33.1 35 29.2 32 27.7 33 27.2 17 29 27
LocalSamplingAndKnnClassification18.320.616.118.112.6 16 16 8 12.4 2 5.8 10 8.1 11 8 15 4.5 13 5.5 17 4.1 13 2.2 24 5.1 22 3.4 29 8.1 31 10.5 24 15.6 30 7.3 30 12.3 29 9.4 29 24.1 18 21.8 9 19.7 10 24.7 23 24.8 9 25.9 17
Weighted Color and Texture Matting18.516.420.518.813.1 22 17.8 13 15.8 20 6.5 19 9.4 21 8.6 23 4.2 5 4.7 4 3.9 10 1.7 13 6 28 2.7 13 6.4 17 11 27 16.3 31 4.8 13 9.1 16 6.5 11 23.7 14 24.8 19 23.2 17 26.5 28 40.2 36 28.5 25
CCM18.821.618.416.313.8 26 20.8 23 16.9 28 6.4 18 8.9 19 8.2 19 4.7 18 5.9 22 3.6 5 2.5 28 4.3 17 3 18 7 22 9 15 10.6 8 4.9 14 8.1 10 5.7 8 25.6 22 27.5 25 24.5 20 25.4 25 26.4 16 28.2 24
Shared Matting19.217.522.417.610.8 3 20.5 21 15 14 7.8 32 11.6 33 8.1 16 4.2 4 5.3 11 4.2 18 2.1 19 5.8 26 2.9 16 5.9 10 9.2 17 11.4 12 5 15 8.8 13 6.8 13 34.9 38 34.9 36 34.3 36 23.9 19 28.4 22 25.7 16
LNCLM matting19.421.519.317.510.9 5 11.2 3 16.7 26 6.9 28 8.9 20 7.2 5 5.6 36 7 37 4.1 15 2.5 30 5.1 23 3.5 32 7.5 25 10 23 12 13 5.5 21 11.3 23 7.3 19 25.2 20 23 15 19.9 12 21.2 7 25 10 26.4 18
Global Sampling Matting21.518.424.122.110.9 6 22.7 31 15.4 15 6.3 16 9.5 25 9 30 4.7 19 6.4 31 4.3 24 2.2 25 5.6 25 3.4 28 6.9 21 9.6 19 12.9 20 6.3 27 12.5 30 8.6 27 25.8 23 27.5 24 25.3 23 22 10 24.4 8 23.7 10
Segmentation-based matting22.723.421.82312.8 20 23.5 33 16.6 25 6.6 21 8.3 14 7.3 8 4.8 26 6.1 25 4.3 25 2.1 20 3.9 16 3.1 21 6.7 19 8 7 13.4 23 6 25 8.8 14 8.2 24 31.6 34 35.6 37 38.8 38 24.5 22 32 28 26.7 20
SRLO Matting22.821.524.622.114.7 29 18 14 17.7 30 6.9 27 10.7 29 8.9 28 4.9 28 5.7 19 4.7 32 2.1 21 6.5 29 2.8 15 6.3 15 10.9 26 15.2 29 5.4 16 11.6 27 7 17 26.5 28 29.7 29 25.1 22 21.7 8 28.5 24 22.3 4
Improved color matting23.723.82324.314.9 30 24.5 35 20 36 6.7 23 9.5 22 8.5 22 4.6 15 6.1 26 4.3 26 2.6 32 5.4 24 3.4 30 7.5 26 9.9 21 12.5 17 6 23 10.1 21 8.4 26 26.1 24 26.7 23 23.6 18 23.8 17 25.6 12 26.7 19
Local Spline Regression (LSR)24.226.320.825.512.2 13 20 19 16.2 21 6.1 14 8.8 18 8.1 17 5.2 32 6.2 28 4.6 29 2.2 26 4.9 21 3.1 23 9.4 36 11.9 30 18.3 34 8.2 33 11.4 24 10.1 31 26.4 26 22.5 12 20.2 13 27 30 26 14 39.8 36
Global Sampling Matting (filter version)24.522.526.824.412.3 14 24.3 34 16.3 22 7.3 30 10.2 28 9.5 33 5.1 31 6.4 30 4.7 33 2.4 27 4.7 20 3.2 25 5.9 7 9.5 18 12.4 15 6.5 28 13.1 31 8.3 25 26.5 27 28.3 28 30 34 23.6 16 29.1 25 23.5 8
KNN Matting24.726.625.921.516.2 34 19.7 17 16.8 27 8 33 11 30 9 32 4.7 20 6.7 34 4.3 21 3 35 7.7 35 3.7 33 9.2 34 11.3 28 11.3 11 6 24 10.4 22 6.7 12 18.1 2 19.6 4 17 5 27.4 31 41 37 32.7 31
Learning Based Matting25.726.124.826.316 33 22 29 18.7 31 6.6 22 7.4 7 7.4 9 4.8 24 6.1 24 4.3 27 2.1 18 3.7 15 2.8 14 7.5 27 14.5 36 19.5 37 8.6 37 14.1 34 14.6 39 22.5 11 24.8 18 19.9 11 34.6 37 38.5 35 51.2 42
LMSPIR26.525.527.926.315.2 32 20 18 19.1 33 6.7 24 11.2 31 8.7 26 4.8 25 5.8 20 4.6 31 2.1 22 6.8 32 3 20 7.9 29 14.3 35 20.2 38 5.5 19 11.4 25 7 16 29.7 32 31 31 29.6 33 24 21 34.2 31 25 13
Shared Matting (Real Time)26.725.427.62712.4 15 21.6 25 16.3 23 9.5 36 13.5 35 9.9 34 4.4 8 5.6 18 4.4 28 2.5 31 6.8 33 3.2 26 7.1 23 10.8 25 12.6 18 5.4 18 9.7 18 7.4 21 35.5 40 35.8 38 35.5 37 27.6 32 33.4 29 29.8 29
Closed-Form Matting27.32624.930.912.7 18 21.9 28 17.2 29 5.9 11 8.5 16 8.6 25 4.7 21 6 23 4.3 23 2.2 23 4.6 18 3.3 27 9.3 35 12.1 31 19.3 36 8.3 34 14.9 36 13.4 38 34.2 37 32.4 34 27.4 29 26.5 29 25.7 13 48.3 40
Improving Sampling Criterion31.230.432.630.612.7 19 21.1 24 16.4 24 11.5 39 17 40 12.1 39 5.8 38 8.1 40 5.7 38 5.4 43 12.1 42 6.6 42 9.6 38 15.7 37 17.8 32 10.2 39 17 37 11.2 36 23.8 16 26.7 22 25.9 25 22.2 11 27.3 19 23.7 9
Cell-based matting Laplacian31.333.43030.415.1 31 21.7 26 15.6 16 7.6 31 11.4 32 9 31 5.7 37 6.7 35 5.2 36 3.1 36 6.7 30 4.5 36 8.6 32 11.4 29 14.9 27 8.5 36 13.5 33 11.1 34 27.7 29 26.2 21 27.6 30 32.4 35 37.6 34 37.9 33
Large Kernel Matting31.533.130.930.517.2 35 21.8 27 20.7 37 7.2 29 9.6 26 8.4 21 5.3 33 6.6 33 4.6 30 2.9 34 8.2 37 4.2 34 8.6 33 12.1 32 14.7 26 8 32 13.4 32 11.2 35 33 35 31.8 33 26.1 26 32.1 34 32 27 38.4 35
Robust Matting32.328.833.13517.3 36 28.4 39 21.1 38 10.1 37 16.9 39 11.4 37 4.8 23 6.5 32 5 35 2.8 33 7.3 34 4.4 35 7.3 24 14 34 18.1 33 6.8 29 14.6 35 10.6 33 22.7 12 26.1 20 32.1 35 34.4 36 37 32 38 34
SPS matting34.333.837.331.913.4 23 23 32 14.8 11 12.5 40 18.5 41 12.4 40 6.6 42 9.4 43 6.5 43 4.5 41 12.7 43 5.4 39 9.5 37 16.4 39 18.6 35 9.8 38 18.1 40 10.6 32 26.1 25 30.9 30 26.2 27 25 24 33.5 30 29.5 28
High-res matting35.233.836.535.418.6 38 25.8 36 24.6 40 8.6 34 14.1 36 11.1 36 5 29 6.2 27 4.8 34 2.5 29 8.3 38 3.2 24 7.8 28 14 33 21.4 39 8.5 35 18.1 41 12.2 37 35.3 39 38.1 40 42.6 41 38.7 38 54.6 41 36.8 32
Transfusive Weights36.13635.836.523.2 40 26.7 38 22.4 39 9.2 35 11.9 34 10.4 35 6.1 40 8.1 39 5.9 40 3.8 39 8 36 5.3 38 12.9 41 21.4 41 37.6 45 13.1 44 22.8 43 18.3 44 20.9 7 22.9 13 19 8 59.5 42 65.7 42 72.8 43
Random Walk Matting38.940.536.539.817.9 37 20.3 20 19.4 34 11.3 38 15.6 37 11.8 38 5.8 39 7 36 6.3 42 3.4 37 6.7 31 4.6 37 13.1 43 22.1 42 27.4 41 12.3 43 18 39 15.7 42 44.1 44 43.5 43 41 40 75.1 43 81.8 44 80.6 44
Geodesic Matting40.14139.939.526.9 43 38.5 44 32.5 43 14.2 41 16.5 38 17.4 42 11.7 45 14 46 9.4 45 7.6 45 15.1 44 8.7 45 12.8 40 16.7 40 15.1 28 7.3 31 12.1 28 9.8 30 37.3 42 37.4 39 42.8 42 48.6 41 50 40 48.6 41
Iterative BP Matting41.240.541.14223.6 41 29.9 40 27.2 41 16.7 42 24.3 43 20.7 45 6.7 43 9 41 6.3 41 3.8 38 11.3 40 6.8 44 14.1 44 22.8 43 27.9 42 11.4 41 19 42 14.7 40 33.4 36 39.3 41 47.5 45 40.6 39 48.1 39 45.1 38
Easy Matting41.6424141.923.9 42 32.6 42 30 42 17.1 43 21.8 42 19.4 44 6.3 41 7.5 38 5.8 39 4.7 42 10.5 39 5.6 40 12.1 39 15.7 38 22.9 40 11.2 40 17 38 14.8 41 49.5 45 49.6 45 46.2 44 77.8 44 108.6 46 109.2 45
Improved Bayesian42.14242.541.931.2 45 34.9 43 33.9 45 17.8 44 24.4 44 17.2 41 5.5 35 9.3 42 5.3 37 3.9 40 11.4 41 6.6 43 13 42 29.5 44 34.6 44 11.9 42 24.5 44 16.1 43 42.9 43 44.6 44 46 43 92.4 45 46.5 38 45.4 39
Bayesian Matting43.443.544.442.330.3 44 42.4 45 33.4 44 19.2 45 25.8 45 18.4 43 10.8 44 12.4 44 10.8 46 6.6 44 18.5 46 6.2 41 14.2 45 29.8 45 33.2 43 15.4 45 30.6 45 19.7 45 35.8 41 40.6 42 39.6 39 45.3 40 76.8 43 43.6 37
Poisson Matting45.84645.645.851.8 46 56.2 46 52 46 28.3 46 43.5 46 30.7 46 12.1 46 13.7 45 9.2 44 11.7 46 18.4 45 11.2 46 22.4 46 36.8 46 55.5 46 21.4 46 32.2 46 22.7 46 53.6 46 72.9 46 58.4 46 125.5 46 84.8 45 139.7 46
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References

MethodReference and notesImplementation details
Closed-Form MattingA. Levin, D. Lischinski, Y. Weiss, A Closed Form Solution to Natural Image Matting, CVPR, 2006Matlab implementation on a Intel Core2 Quad with 2.4 GHZ
Bayesian MattingY.Y. Chuang, B. Curless, D. Salesin, R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001C++ implementation on a Intel Core2 Quad with 2.4 GHZ
Poisson MattingJ. Sun, J. Jia, C.K. Tang, H.Y. Shum, Poisson matting, SIGGRAPH, 2004Matlab implementation on a Intel Core2 Quad with 2.4 GHZ
Easy MattingY. Guan, W. Cheny, X. Liang, Z. Ding, Q. Peng, Easy Matting: A Stroke Based Approach for Continuous Image Matting, Eurographics, 2006C++ implementation on a Intel Core2 Quad with 2.4 GHZ
Random Walk MattingL. Grady, T. Schiwietz, S. Aharon, Random Walks For Interactive Alpha-Matting, VIIP, 2005Matlab/C++ implementation on a Intel Core2 Quad with 2.4 GHZ
Robust MattingJ. Wang, M. Cohen, Optimized Color Sampling for Robust Matting, CVPR, 2007C++ implementation on a Intel Core2 Quad with 2.4 GHZ
Geodesic MattingXue Bai, Guillermo Sapiro, A geodesic framework for fast interactive image and video segmentation and matting, ICCV 2007C++ implementation on a Intel Core2 Duo with 2.53 GHZ
Iterative BP MattingJue Wang, Michael Cohen, Jue Wang and Michael F. Cohen. An iterative optimization approach for unified image segmentation and matting. ICCV 2005.c++ implementation on a Intel Core2 Quad with 3 GHZ
Improved color mattingC. Rhemann, C. Rother, M. Gelautz, Improving Color Modeling for Alpha Matting. BMVC, 2008Matlab implementation on a Intel Core2 Duo with 2.4 GHZ
High-res mattingC. Rhemann, C. Rother, A. Rav-Acha, M. Gelautz, T. Sharp, High ResolutionMatting via Interactive Trimap Segmentation. CVPR, 2008Matlab/C++ implementation on a Intel Core2 Duo with 2.4 GHZ
Large Kernel MattingKaiming He, Jian Sun, and Xiaoou Tang, Fast Matting using Large Kernel Matting Laplacian Matrices, CVPR 2010C++ implementation on a Intel Core Duo with 2 GHZ
Segmentation-based mattingChristoph Rhemann, Carsten Rother, Pushmeet Kohli, Margrit Gelautz, A Spatially Varying PSF-based Prior for Alpha Matting, CVPR 2010Matlab/C++ implementation on a Intel Core2 Quad with 2.39 GHZ
Shared MattingEduardo S. L. Gastal and Manuel M. Oliveira, Shared Sampling for Real-Time Alpha Matting, Eurographics, 2010C++/GLSL implementation on a Core 2 Quad with 2.8 GHZ
Shared Matting (Real Time)Eduardo S. L. Gastal and Manuel M. Oliveira, Shared Sampling for Real-Time Alpha Matting, Eurographics, 2010C++/GLSL implementation on a Core 2 Quad with 2.8 GHZ
Learning Based MattingYuanjie Zheng, Chandra Kambhamettu, Yuanjie Zheng, Chandra Kambhamettu. Learning Based Digital Matting. ICCV 2009. SOURE CODEMatlab/C++ implementation on a Intel Core2 Duo with 2.53 GHZ
LMSPIRBei He, Guijin Wang, Zhiwei Ruan, Xuanwu Yin, Xiaokang Pei, Xinggang Lin, Local Matting based on Sample-pair Propagation and Iterative Refinement, ICIP 2012C++ implementation on a Intel Core2 Dual with 2 GHZ
SVR MattingZhanpeng Zhang, Qingsong Zhu, Yaoqin Xie, Learning Based Alpha Matting using Support Vector Regression, ICIP 2012Matlab implementation on a Intel Pentium Dual-Core with 3 GHZ
Cell-based matting LaplacianChen-Yu Tseng and Sheng-Jyh Wang, A cell-based matting Laplacian for contrast enhancement, ICIP 2012C++ implementation on a Intel Core i3 with 3 GHZ
Global Sampling MattingKaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun, A Global Sampling Method for Alpha Matting, CVPR 2011C++ implementation on a Intel Core2 with 2 GHZ
Global Sampling Matting (filter version)Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun, A Global Sampling Method for Alpha Matting, CVPR 2011 (using the guided filter in "Guided Image Filtering", ECCV 2010, by Kaiming He, Jian Sun, and Xiaoou Tang )C++ implementation on a Intel Core2 with 2 GHZ
Local Spline Regression (LSR)Shiming Xiang, Feiping Nie, Changshui Zhang, Semi-Supervised Classification via Local Spline Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 11, pp. 2039-2053, 2010C++ implementation on a Intel Core2 with 3 GHZ
Weighted Color and Texture MattingE.Shahrian and D.Rajan, Weighted Color and Texture Sample Selection for Image Matting , CVPR 2012. matlab implementation on a Intel(R) Xeon(R) with 2.93 GHZ
KNN MattingQifeng Chen, Dingzeyu Li, Chi-Keung Tang, KNN Matting, CVPR 2012Matlab implementation on a Intel Core 2 Duo with 2.13 GHZ
SRLO MattingBei He, Guijin Wang, Xuanwu Yin, Bo Liu, Chenbo Shi, Xinggang Lin, High-accuracy and Quick Matting based on Sample-pair Refinement and Local Optimization, IEICE trans. on Information & Systems, 2013C++ implementation on a Intel Core2 Dual with 2 GHZ
LNSP MattingXiaowu Chen, Dongqing Zou, Ping Tan, Image Matting with Local and Nonlocal Smooth Priors, CVPR 2013matlab implementation on a intel core2 with 2.2 GHZ
CCMYongfang Shi, Au, O.C., Jiahao Pang, Tang, K., Wenxiu Sun, Hong Zhang, Wenjing Zhu, and Luheng Jia, Color Clustering Matting, ICME2013.Matlab implementation on a Intel Core i7 with 2.8 GHZ
Iterative Transductive MattingBei He, Guijin Wang, Chenbo Shi, Xuanwu Yin, Bo Liu, Xinggang Lin, Iterative Transductive Matting, ICIP 2013Matlab implementation on a Intel Core2 Dual with 2.2 GHZ
Improving Sampling CriterionJun Cheng, Zhenjiang Miao, Improving Sampling Criterion for Alpha Matting, RACVPR2013 in Conjunction with ACPR2013C++ implementation on a Core i5 with 2.5 GHZ
Transfusive WeightsKaan Yucer, Alexander Sorkine-Hornung, and Olga Sorkine-Hornung, Transfusive Weights for Content-Aware Image Manipulation, VMV2013Matlab implementation on a Quad-Core Intel Xeon with 3.2 GHZ
Comprehensive samplingE.Shahrian, D.Rajan, B.Price, S.Cohen, Improving Image Matting using Comprehensive Sampling Sets, CVPR 2013.Matlab implementation on a Intel Xeon with 2.4 GHZ
Comprehensive Weighted Color and TextureE.Shahrian , D.Rajan, Weighted Color and Texture Sample Selection for Image Matting, IEEE Transaction on Image Processing , Volume:PP, Issue: 99 , 2013. Matlab implementation on a Intel i7 with 3.5 GHZ
SPS mattingAhmad Al-Kabbany and Eric Dubois, "Improved global-sampling matting using sequential pair-selection strategy", In Proc. Visual Information Processing andCommunication V(SPIE), San Francisco, February 2014.Matlab implementation on a Intel Core2 Quad with 2.66 GHZ
Improved BayesianWenshuang Tan, Automatic Matting of Identification Photos, CAD/Graphics, 2013C++ implementation on a Intel Core(TM)i7-2600 with 3.4 GHZ
Sparse coded mattingJubin Johnson, Deepu Rajan, Hisham Cholakkal, Sparse Codes as Alpha Mattes, BMVC 2014.Matlab implementation on a Intel Xeon with 3.2 GHZ
LNCLM mattingB.-K. Kim, M. Jin, W.-J Song, Local and Nonlocal Color Line Models for Image Matting, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E97-A. no.8, pp. 1814-1819, Aug. 2014.MATLAB implementation on a Intel Core i5-3570 with 3.4 GHZ
Graph-based sparse mattingJubin Johnson, Ehsan Shahrian Varnousfaderani, Hisham Cholakkal, and Deepu Rajan, Sparse Coding for Alpha Matting, IEEE Transactions on Image Processing, Volume: PP, Issue: 99, 2016.Matlab implementation on a Intel Xeon with 3.2 GHZ
KL-Divergence Based Sparse SamplingLevent Karacan, Aykut Erdem and Erkut Erdem, Image Matting with KL-Divergence Based Sparse Sampling,IEEE International Conference on Computer Vision(ICCV) 2015)Matlab implementation on a Intel Xeon(R) CPU E5-2620 with 2 GHZ
LocalSamplingAndKnnClassificationXiao Chen, Fazhi He, A Sampling-Propagation Matting Method Based on the Sample Validity and KNN Classification Labeling, Journal of Computer-Aided Design and Computer Graphics (CADCG), vol. 28(12), pp. 2186-2194, 2016.matlab implementation on a intel i3 2120 with 3.3 GHZ
DCNN MattingDonghyeon Cho, Yu-Wing Tai, Inso Kweon, Natural Image Matting using Deep Convolutional Neural Networks. ECCV 2016matlab implementation on a Intel Core i7 with 3.4 GHZ
CSC MattingXiaoxue Feng, Xiaohui Liang, Zili Zhang, A Cluster Sampling Method for Image Matting via Sparse Coding. ECCV 2016 Matlab implementation on a Intel(R) Core(TM) i5-3470 with 3.2 GHZ
Patch-based MattingGuangying Cao, Jianwei Li, Zhiqiang He, Xiaowu Chen, Divide and Conquer: A Self-Adaptive Approach for High-Resolution Image Matting. International Conference on Virtual Reality and Visualization (ICVRV 2016)matlab implementation on a i7 with 3.6 GHZ
TSPS-RV MattingAhmad Al-Kabbany and Eric Dubois, Matting with Sequential Pair Selection Using Graph Transduction. The 21st International Symposium on Vision, Modeling, and Visualization (VMV 2016)Matlab implementation on a Intel Core2 Quad with 2.66 GHZ
Deep MattingNing Xu, Brian Price, Scott Cohen and Thomas Huang, Deep Image Matting, CVPR 2017Matlab implementation on a I7 with 2.7 GHZ
Information-flow mattingYagiz Aksoy, Tunc Ozan Aydin and Marc Pollefeys, Designing Effective Inter-Pixel Information Flow for Natural Image Matting, CVPR 2017Matlab implementation on a Intel Xeon with 3.5 GHZ
Three-layer graph modelChao Li, Ping Wang, Xiangyu Zhu, Huali Pi, Submitted to Computer Vision and Image UnderstandingC++, Matlab implementation on a Intel Xeon E5-2620 v3 with 2.4 GHZ
ATPM MattingXiangyu Zhu, Ping Wang, Zhenghai Huang, Submitted to Soft Computingmatlab implementation on a Intel Xeon with 2.4 GHZ