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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 | |
TMFNet | 3.4 | 2.4 | 2.9 | 5 | 6.1 1 | 6.5 1 | 8.3 3 | 4.2 2 | 4.5 4 | 5 4 | 2.6 3 | 2.7 3 | 2.4 4 | 0.8 1 | 0.8 2 | 1.3 6 | 4 2 | 5.2 4 | 6.6 6 | 1.7 2 | 1.9 3 | 2.4 4 | 14.4 1 | 14.6 1 | 14.3 1 | 17.5 7 | 18 5 | 21.7 12 |
IamAlpha | 4.5 | 5.3 | 4 | 4.3 | 8 5 | 8.4 4 | 8.4 4 | 4.4 6 | 4.4 3 | 4.4 1 | 2.7 4 | 2.7 4 | 2.7 7 | 0.9 5 | 0.9 3 | 0.9 1 | 4.8 9 | 5.2 5 | 6.2 4 | 2.5 10 | 2.7 9 | 3.1 10 | 15.8 2 | 15.8 2 | 15.8 6 | 15.9 1 | 16.5 2 | 16.5 1 |
LFPNet | 4.8 | 4.1 | 3.6 | 6.8 | 7.6 4 | 8.1 3 | 9 8 | 4.3 5 | 4.3 2 | 5.1 6 | 3 11 | 3 9 | 2.8 11 | 0.8 2 | 0.8 1 | 1.5 10 | 3.9 1 | 4.2 1 | 5.3 1 | 1.7 1 | 1.7 1 | 2.2 1 | 17 5 | 18 9 | 17.9 15 | 16.6 4 | 16.6 3 | 17.6 2 |
TIMI-Net | 5.4 | 6.6 | 6.3 | 3.3 | 8.3 7 | 8.7 7 | 9 7 | 4.4 7 | 4.7 6 | 4.4 3 | 2.8 8 | 2.9 6 | 2 2 | 1 12 | 1.1 9 | 1.3 4 | 4.7 6 | 5.2 6 | 6.2 3 | 1.8 3 | 1.9 2 | 2.3 2 | 15.9 4 | 16.2 3 | 15.5 2 | 16.6 6 | 19.2 11 | 18 3 |
SIM | 6.4 | 6.9 | 5.5 | 6.8 | 8.3 6 | 8.7 6 | 9 6 | 4.8 16 | 4.8 12 | 6 19 | 2.2 1 | 2.2 1 | 2 1 | 0.9 8 | 0.9 6 | 1.1 2 | 4.7 7 | 5.1 3 | 6.3 5 | 2.2 6 | 2.3 6 | 2.5 5 | 15.9 3 | 16.3 4 | 16.3 8 | 17.8 8 | 18 6 | 20.9 8 |
RMat | 7.1 | 6.3 | 6 | 9 | 7.5 3 | 7.5 2 | 7.9 2 | 3.7 1 | 3.8 1 | 5.3 10 | 2.8 5 | 2.9 7 | 2.3 3 | 0.9 6 | 0.9 4 | 1.4 8 | 4.2 3 | 4.3 2 | 5.5 2 | 1.9 4 | 2 4 | 2.4 3 | 20.5 26 | 20.9 27 | 22.4 37 | 16 2 | 16.2 1 | 20.6 7 |
PIIAMatting | 10.2 | 7 | 12.6 | 11 | 9.1 13 | 10 15 | 9.3 11 | 4.3 4 | 4.6 5 | 5.4 11 | 2.8 7 | 3.7 18 | 2.7 9 | 0.9 4 | 2.3 24 | 2 17 | 4.7 8 | 6.8 13 | 8.3 12 | 2.2 8 | 2.7 11 | 4.3 19 | 17.2 7 | 17.3 6 | 15.5 3 | 16.6 5 | 19.1 9 | 20.2 6 |
LSA Matting | 10.9 | 10.3 | 11.9 | 10.5 | 8.4 8 | 9 8 | 8.8 5 | 4.5 9 | 5.7 22 | 5 5 | 3.3 15 | 3.5 13 | 3.1 15 | 1 9 | 1 8 | 1.6 11 | 5.4 15 | 7.1 21 | 8.5 15 | 2.7 15 | 3 12 | 3.3 12 | 17.3 8 | 17.3 7 | 16.8 10 | 16.3 3 | 17.4 4 | 21.6 11 |
HDMatt | 12 | 13.4 | 10.8 | 11.8 | 9.5 15 | 10 14 | 10.7 16 | 4.7 11 | 4.8 10 | 5.8 17 | 2.9 10 | 3 8 | 2.6 6 | 1.1 15 | 1.2 14 | 1.3 5 | 5.2 12 | 5.9 8 | 6.7 7 | 2.4 9 | 2.6 7 | 3.1 9 | 17.3 9 | 17.3 5 | 17 12 | 21.5 26 | 22.4 20 | 23.2 22 |
LiteMatting | 12.9 | 11.5 | 10.1 | 17 | 8.6 9 | 9.1 9 | 9.2 9 | 4.6 10 | 4.7 7 | 5.6 14 | 3.5 19 | 3.7 15 | 3.2 19 | 1 11 | 1 7 | 2.6 31 | 4.5 4 | 5.3 7 | 7.8 9 | 2.1 5 | 2.2 5 | 3 7 | 18.8 19 | 19.4 21 | 19.5 26 | 20 15 | 19.2 10 | 22.8 21 |
TransMatting: Enhancing Transparent ... | 13.6 | 16.1 | 11.3 | 13.4 | 7.5 2 | 8.6 5 | 7.8 1 | 4.9 22 | 5 15 | 5.5 13 | 3.4 16 | 3.5 14 | 3.2 17 | 1.8 33 | 1.8 20 | 2.2 23 | 5.7 21 | 6.3 9 | 8.4 13 | 2.6 14 | 2.7 10 | 3 8 | 17.6 11 | 18 10 | 18.9 22 | 18.3 10 | 18.3 7 | 21.5 10 |
AdaMatting | 14.3 | 12.8 | 13.3 | 16.9 | 10.2 18 | 11.1 20 | 10.8 17 | 4.9 18 | 5.4 18 | 6.6 23 | 3.6 20 | 3.4 12 | 3.4 23 | 0.9 3 | 0.9 5 | 1.8 13 | 4.7 5 | 6.8 11 | 9.3 23 | 2.2 7 | 2.6 8 | 3.3 11 | 19.2 22 | 19.8 24 | 18.7 21 | 17.8 9 | 19.1 8 | 18.6 4 |
A2U Matting | 14.4 | 13.1 | 11.8 | 18.4 | 9.3 14 | 9.7 12 | 10.9 18 | 4.8 14 | 4.9 13 | 5.3 9 | 3 12 | 3.1 10 | 2.8 10 | 1 13 | 1.1 10 | 1.4 9 | 5.1 10 | 6.7 10 | 8.5 14 | 2.5 12 | 3 13 | 5.9 30 | 17.3 10 | 18.4 12 | 18.1 16 | 20.6 20 | 20.6 14 | 27.3 41 |
SampleNet Matting | 15 | 11.8 | 15.3 | 17.9 | 9.1 12 | 9.7 13 | 9.8 13 | 4.3 3 | 4.8 9 | 5.1 7 | 3.4 18 | 3.7 19 | 3.2 20 | 0.9 7 | 1.1 12 | 2 15 | 5.1 11 | 6.8 12 | 9.7 26 | 2.5 11 | 4 20 | 3.7 14 | 18.6 18 | 19.3 20 | 19.1 25 | 20 14 | 21.6 17 | 23.2 23 |
FGI Matting | 15 | 18.1 | 14.8 | 12.1 | 10.3 20 | 10.4 17 | 11.7 22 | 4.9 21 | 5.3 17 | 5.2 8 | 2.5 2 | 2.7 2 | 2.4 5 | 1.2 19 | 1.2 15 | 1.2 3 | 5.6 18 | 6.9 14 | 7.8 10 | 2.6 13 | 3.1 14 | 3 6 | 18.9 21 | 18.9 14 | 20 30 | 22.3 31 | 23.6 25 | 21.7 13 |
GCA Matting | 16.5 | 17.1 | 14.5 | 17.8 | 8.8 10 | 9.5 11 | 11.1 20 | 4.9 17 | 4.8 11 | 5.8 18 | 3.4 17 | 3.7 17 | 3.2 18 | 1.1 18 | 1.2 13 | 1.3 7 | 5.7 20 | 6.9 15 | 7.6 8 | 2.8 17 | 3.1 15 | 4.5 22 | 18.3 15 | 19.2 16 | 18.5 19 | 20.8 23 | 21.7 18 | 24.7 30 |
ATNet Matting | 17.8 | 19.4 | 17 | 17 | 9.7 17 | 10.2 16 | 9.6 12 | 4.5 8 | 4.8 8 | 4.4 2 | 3.7 21 | 3.9 20 | 3.4 24 | 1.7 30 | 1.9 21 | 2.2 22 | 5.9 26 | 7 17 | 9.3 22 | 2.9 18 | 3.3 17 | 3.9 16 | 19.8 24 | 20.6 25 | 20.7 33 | 19 11 | 19.2 12 | 20.1 5 |
VDRN Matting | 18 | 20.1 | 18.3 | 15.8 | 8.9 11 | 9.4 10 | 9.3 10 | 5.2 23 | 5.6 21 | 6.6 22 | 2.8 9 | 3.3 11 | 2.7 8 | 1.8 34 | 1.9 22 | 2 14 | 5.7 19 | 7.1 18 | 8.3 11 | 3 19 | 3.5 18 | 3.6 13 | 17.6 12 | 18.3 11 | 16.9 11 | 23.2 34 | 25.9 35 | 26.5 37 |
Deep Matting | 18.5 | 19.4 | 18.1 | 17.9 | 10.7 22 | 11.2 22 | 11 19 | 4.8 15 | 5.8 23 | 5.6 15 | 2.8 6 | 2.9 5 | 2.9 12 | 1.1 14 | 1.1 11 | 2 16 | 6 31 | 7.1 19 | 8.9 18 | 2.7 16 | 3.2 16 | 3.9 15 | 19.2 23 | 19.6 23 | 18.7 20 | 21.8 28 | 23.9 26 | 24.1 28 |
Information-flow matting | 20.6 | 21.8 | 21.9 | 18.3 | 10.3 19 | 11.2 21 | 12.5 24 | 5.6 29 | 7.3 27 | 7.3 28 | 3.8 22 | 4.1 22 | 3 14 | 1.4 21 | 2.3 23 | 2 19 | 5.9 28 | 7.1 20 | 8.6 16 | 3.6 23 | 5.7 24 | 4.6 23 | 18.3 14 | 19.3 19 | 15.8 5 | 20.2 18 | 22.2 19 | 22.3 17 |
IndexNet Matting | 21.7 | 23.6 | 20.8 | 20.6 | 12.6 39 | 13.4 24 | 11.4 21 | 4.8 13 | 4.9 14 | 5.7 16 | 3.3 14 | 4 21 | 3 13 | 1.1 16 | 1.5 17 | 1.6 12 | 6.4 35 | 7.5 24 | 8.9 19 | 3.4 20 | 4 19 | 4.1 17 | 18.6 17 | 19.1 15 | 18.5 18 | 23.4 35 | 25.1 32 | 29.3 49 |
DCNN Matting | 23.1 | 25.1 | 21.6 | 22.6 | 12 32 | 14.1 27 | 14.5 31 | 5.3 24 | 6.4 24 | 6.8 25 | 3.9 25 | 4.5 25 | 3.4 22 | 1.6 27 | 2.5 25 | 2.2 25 | 6 30 | 6.9 16 | 9.1 20 | 4 26 | 6 25 | 5.3 25 | 19.9 25 | 19.2 18 | 19.1 24 | 19.4 12 | 20 13 | 21.2 9 |
AlphaGAN | 23.5 | 24.1 | 24.4 | 22.1 | 9.6 16 | 10.7 18 | 10.4 15 | 4.7 12 | 5.3 16 | 5.4 12 | 3.1 13 | 3.7 16 | 3.1 16 | 1.1 17 | 1.3 16 | 2 18 | 6.4 37 | 8.3 33 | 9.3 24 | 3.6 22 | 5 21 | 4.3 20 | 20.8 27 | 21.5 28 | 20.6 32 | 25.7 49 | 28.7 47 | 26.7 40 |
CDI-Net | 26 | 26.5 | 20.9 | 30.6 | 12.6 37 | 13.7 25 | 14 29 | 4.9 20 | 5.5 19 | 6.6 24 | 3.9 24 | 4.3 23 | 3.7 28 | 1.3 20 | 1.5 18 | 2.2 21 | 6.8 42 | 8.1 30 | 9.9 27 | 4.3 32 | 5.6 23 | 8.1 45 | 18.9 20 | 18.7 13 | 18.4 17 | 20.2 17 | 21.5 16 | 36.1 54 |
Context-aware Matting | 26.1 | 30.1 | 24.4 | 23.8 | 10.4 21 | 11.1 19 | 10.1 14 | 6.4 39 | 7.4 30 | 7.1 26 | 4.1 26 | 4.5 26 | 3.8 31 | 2.3 49 | 3.1 27 | 3 42 | 7.1 45 | 8.2 31 | 9.1 21 | 3.5 21 | 5.5 22 | 4.1 18 | 18.3 16 | 19.2 17 | 16.5 9 | 21.1 24 | 23.3 23 | 24.6 29 |
Three-layer graph matting | 28.4 | 23.6 | 26.5 | 35 | 10.7 23 | 15.2 28 | 13.8 27 | 4.9 19 | 5.6 20 | 8.1 40 | 3.9 23 | 4.4 24 | 3.6 27 | 1 10 | 1.8 19 | 3 40 | 5.9 25 | 7.3 23 | 12.4 40 | 4.2 29 | 8 32 | 8.5 49 | 24.2 41 | 25.6 42 | 24.2 42 | 20.5 19 | 23.5 24 | 22.2 15 |
ATPM Matting | 30.3 | 33.8 | 33 | 24.3 | 14 50 | 17.8 35 | 13.4 25 | 5.5 25 | 6.4 25 | 7.3 29 | 5.4 57 | 6.4 52 | 4.3 45 | 1.7 31 | 3.3 32 | 2.3 28 | 6.8 41 | 8 27 | 8.7 17 | 4.2 27 | 7.5 29 | 5.5 26 | 17.2 6 | 17.6 8 | 15.7 4 | 22.6 33 | 37.3 56 | 22.8 20 |
Three Stages Matting | 32 | 31.3 | 32.6 | 32.1 | 11.7 31 | 13.9 26 | 13.9 28 | 5.6 26 | 7.4 29 | 7.9 34 | 4.6 38 | 5.5 39 | 4.2 40 | 2.2 45 | 4 39 | 3.1 45 | 6.5 38 | 11 50 | 11.9 35 | 4 24 | 6.5 26 | 4.5 21 | 23.3 35 | 23.2 37 | 22.3 36 | 19.6 13 | 20.8 15 | 22.4 18 |
LNSP Matting | 33.5 | 29.9 | 34.3 | 36.4 | 12.2 33 | 22.5 53 | 19.5 58 | 5.6 27 | 8.1 33 | 8.8 50 | 4.6 36 | 5.9 44 | 3.6 26 | 1.5 24 | 3.5 35 | 3.1 44 | 6.2 32 | 8.1 29 | 10.7 31 | 4 25 | 7.1 27 | 6.4 32 | 21.5 30 | 20.8 26 | 16.3 7 | 22.5 32 | 24.4 27 | 27.8 43 |
CSC Matting | 33.7 | 37.1 | 30.1 | 33.9 | 13.6 47 | 15.6 29 | 14.5 30 | 6.2 37 | 7.5 31 | 8.1 41 | 4.6 39 | 4.8 28 | 4.2 43 | 1.8 36 | 2.7 26 | 2.5 30 | 5.5 17 | 7.3 22 | 9.7 25 | 4.6 34 | 7.6 30 | 6.9 36 | 23.7 37 | 23 35 | 21 34 | 26.3 50 | 27.2 40 | 25.2 32 |
Graph-based sparse matting | 34.8 | 35.1 | 35.6 | 33.5 | 12.6 40 | 20.5 45 | 14.8 35 | 5.7 31 | 7.3 26 | 6.4 21 | 4.5 34 | 5.3 35 | 3.7 29 | 1.4 23 | 3.3 33 | 2.3 27 | 6.3 34 | 7.9 25 | 11.1 32 | 4.2 28 | 8.3 35 | 6.4 31 | 28.7 54 | 31.3 55 | 27.1 51 | 23.6 37 | 25.1 31 | 27.3 42 |
KL-Divergence Based Sparse Sampling | 34.9 | 33.1 | 35.1 | 36.4 | 11.6 30 | 17.5 34 | 14.7 32 | 5.6 28 | 8.5 38 | 8 36 | 4.9 50 | 5.3 33 | 3.7 30 | 1.5 25 | 3.5 34 | 2.1 20 | 5.8 23 | 8.3 32 | 14.1 48 | 5.6 45 | 9.3 40 | 8 44 | 24.6 42 | 27.7 49 | 28.9 54 | 20.7 22 | 22.7 21 | 23.9 27 |
Patch-based Matting | 35 | 28.8 | 37.5 | 38.6 | 10.9 25 | 19 39 | 15.7 41 | 6 34 | 9.5 47 | 8.3 43 | 4.3 30 | 5.2 32 | 4.2 42 | 1.6 26 | 3.2 30 | 2.6 32 | 5.2 13 | 9 38 | 12.4 37 | 4.7 35 | 9.7 42 | 7 37 | 21.6 31 | 21.7 29 | 24.9 44 | 23.5 36 | 28.1 43 | 25.6 33 |
TSPS-RV Matting | 36.3 | 34.4 | 36.5 | 38.1 | 11.3 29 | 16.4 32 | 13.7 26 | 6.1 35 | 8.1 35 | 8.6 47 | 4.5 32 | 5.4 37 | 4.1 39 | 1.4 22 | 3.3 31 | 3.5 54 | 7.9 53 | 8.9 36 | 12.4 39 | 6.2 49 | 9 38 | 8.7 51 | 22.8 34 | 23.5 38 | 21.4 35 | 20.7 21 | 28.5 45 | 22.2 14 |
Iterative Transductive Matting | 37 | 37.9 | 37.4 | 35.8 | 13.1 44 | 17.2 33 | 15.6 40 | 5.7 30 | 8.6 40 | 7.8 33 | 5.1 53 | 5.5 38 | 3.9 32 | 1.9 37 | 5.8 50 | 2.6 33 | 6.6 39 | 8.5 34 | 13.8 47 | 5.4 40 | 10 43 | 7.4 41 | 25.5 44 | 24 39 | 23.8 41 | 20.1 16 | 22.7 22 | 22.7 19 |
SVR Matting | 37.4 | 40.4 | 38.3 | 33.5 | 18.7 62 | 30.7 64 | 19.1 55 | 6.8 49 | 7.7 32 | 7.6 32 | 4.7 45 | 5 31 | 3.4 21 | 1.9 38 | 4.7 42 | 2.9 38 | 5.8 22 | 8.7 35 | 10.5 28 | 4.3 30 | 8 33 | 5.6 28 | 21.2 29 | 22.1 32 | 17.1 14 | 25.6 48 | 26.1 37 | 30.6 52 |
Comprehensive sampling | 37.5 | 33.9 | 38.3 | 40.3 | 11.2 28 | 18.5 38 | 14.8 33 | 6.5 43 | 9.5 46 | 8.9 52 | 4.5 33 | 4.9 29 | 4.1 38 | 1.7 29 | 3.1 29 | 2.3 29 | 5.4 16 | 9.8 42 | 13.4 45 | 5.5 43 | 11.5 49 | 7.4 43 | 23.9 39 | 22 31 | 22.8 38 | 23.8 40 | 28 42 | 28.1 44 |
Comprehensive Weighted Color and Texture | 38 | 37 | 39.6 | 37.3 | 14.6 51 | 16 30 | 15.7 42 | 6.8 48 | 10 50 | 7.9 35 | 4.3 29 | 5 30 | 4.1 36 | 1.7 28 | 3.5 36 | 2.2 24 | 5.4 14 | 9.9 44 | 12.8 43 | 4.3 31 | 7.4 28 | 5.2 24 | 28.3 53 | 28.1 50 | 25.4 47 | 24 42 | 30.2 49 | 28.7 47 |
Sparse coded matting | 38.6 | 41.9 | 39.5 | 34.4 | 13.7 48 | 25.8 60 | 14.8 36 | 6.4 40 | 8.2 36 | 6.2 20 | 4.7 40 | 5.4 36 | 4 34 | 1.8 35 | 3.1 28 | 2.3 26 | 5.9 27 | 8 28 | 10.6 29 | 4.5 33 | 8 31 | 5.5 27 | 30.3 56 | 33.1 58 | 29.2 55 | 27.7 56 | 27.2 39 | 29 48 |
LocalSamplingAndKnnClassification | 40.2 | 42.9 | 38.4 | 39.3 | 12.6 38 | 16 31 | 12.4 23 | 5.8 32 | 8.1 34 | 8 37 | 4.5 35 | 5.5 40 | 4.1 35 | 2.2 46 | 5.1 45 | 3.4 52 | 8.1 54 | 10.5 46 | 15.6 53 | 7.3 53 | 12.3 52 | 9.4 52 | 24.1 40 | 21.8 30 | 19.7 27 | 24.7 45 | 24.8 29 | 25.9 35 |
Weighted Color and Texture Matting | 40.8 | 38.3 | 43.3 | 41 | 13.1 45 | 17.8 36 | 15.8 43 | 6.5 42 | 9.4 44 | 8.6 46 | 4.2 28 | 4.7 27 | 3.9 33 | 1.7 32 | 6 51 | 2.7 34 | 6.4 36 | 11 49 | 16.3 54 | 4.8 36 | 9.1 39 | 6.5 33 | 23.7 36 | 24.8 41 | 23.2 39 | 26.5 51 | 40.2 59 | 28.5 46 |
LNCLM matting | 41.1 | 43.6 | 41.1 | 38.6 | 10.9 26 | 11.2 23 | 16.7 49 | 6.9 51 | 8.9 43 | 7.2 27 | 5.6 59 | 7 60 | 4.1 37 | 2.5 53 | 5.1 46 | 3.5 55 | 7.5 48 | 10 45 | 12 36 | 5.5 44 | 11.3 46 | 7.3 40 | 25.2 43 | 23 36 | 19.9 29 | 21.2 25 | 25 30 | 26.4 36 |
CCM | 41.2 | 44.4 | 41.3 | 38 | 13.8 49 | 20.8 46 | 16.9 51 | 6.4 41 | 8.9 42 | 8.2 42 | 4.7 41 | 5.9 45 | 3.6 25 | 2.5 51 | 4.3 40 | 3 39 | 7 44 | 9 37 | 10.6 30 | 4.9 37 | 8.1 34 | 5.7 29 | 25.6 45 | 27.5 48 | 24.5 43 | 25.4 47 | 26.4 38 | 28.2 45 |
Shared Matting | 41.3 | 39.4 | 45.1 | 39.4 | 10.8 24 | 20.5 44 | 15 37 | 7.8 55 | 11.6 56 | 8.1 38 | 4.2 27 | 5.3 34 | 4.2 41 | 2.1 40 | 5.8 49 | 2.9 37 | 5.9 29 | 9.2 39 | 11.4 34 | 5 38 | 8.8 36 | 6.8 35 | 34.9 61 | 34.9 59 | 34.3 59 | 23.9 41 | 28.4 44 | 25.7 34 |
Global Sampling Matting | 43.8 | 40.3 | 46.6 | 44.4 | 10.9 27 | 22.7 54 | 15.4 38 | 6.3 38 | 9.5 48 | 9 53 | 4.7 42 | 6.4 54 | 4.3 47 | 2.2 47 | 5.6 48 | 3.4 51 | 6.9 43 | 9.6 41 | 12.9 44 | 6.3 50 | 12.5 53 | 8.6 50 | 25.8 46 | 27.5 47 | 25.3 46 | 22 29 | 24.4 28 | 23.7 26 |
SRLO Matting | 44.6 | 43.1 | 47.4 | 43.4 | 14.7 52 | 18 37 | 17.7 53 | 6.9 50 | 10.7 52 | 8.9 51 | 4.9 51 | 5.7 42 | 4.7 55 | 2.1 42 | 6.5 52 | 2.8 36 | 6.3 33 | 10.9 48 | 15.2 52 | 5.4 39 | 11.6 50 | 7 39 | 26.5 51 | 29.7 52 | 25.1 45 | 21.7 27 | 28.5 46 | 22.3 16 |
Segmentation-based matting | 45 | 45.8 | 44.1 | 45.1 | 12.8 43 | 23.5 56 | 16.6 48 | 6.6 44 | 8.3 37 | 7.3 30 | 4.8 49 | 6.1 48 | 4.3 48 | 2.1 41 | 3.9 38 | 3.1 43 | 6.7 40 | 8 26 | 13.4 46 | 6 48 | 8.8 37 | 8.2 46 | 31.6 57 | 35.6 60 | 38.8 61 | 24.5 44 | 32 51 | 26.7 39 |
KNN Matting | 46.2 | 48.1 | 48.3 | 42.3 | 16.2 57 | 19.7 40 | 16.8 50 | 8 56 | 11 53 | 9 55 | 4.7 43 | 6.7 57 | 4.3 44 | 3 58 | 7.7 58 | 3.7 56 | 9.2 57 | 11.3 51 | 11.3 33 | 6 47 | 10.4 45 | 6.7 34 | 18.1 13 | 19.6 22 | 17 13 | 27.4 54 | 41 60 | 32.7 53 |
Improved color matting | 46.3 | 46.5 | 45.6 | 46.6 | 14.9 53 | 24.5 58 | 20 59 | 6.7 46 | 9.5 45 | 8.5 45 | 4.6 37 | 6.1 49 | 4.3 49 | 2.6 55 | 5.4 47 | 3.4 53 | 7.5 49 | 9.9 43 | 12.5 41 | 6 46 | 10.1 44 | 8.4 48 | 26.1 47 | 26.7 46 | 23.6 40 | 23.8 39 | 25.6 33 | 26.7 38 |
Local Spline Regression (LSR) | 46.6 | 48.8 | 43.4 | 47.8 | 12.2 34 | 20 42 | 16.2 44 | 6.1 36 | 8.8 41 | 8.1 39 | 5.2 55 | 6.2 51 | 4.6 52 | 2.2 48 | 4.9 44 | 3.1 46 | 9.4 59 | 11.9 53 | 18.3 57 | 8.2 56 | 11.4 47 | 10.1 54 | 26.4 49 | 22.5 33 | 20.2 31 | 27 53 | 26 36 | 39.8 59 |
Global Sampling Matting (filter version) | 46.8 | 44.4 | 49.6 | 46.4 | 12.3 35 | 24.3 57 | 16.3 45 | 7.3 53 | 10.2 51 | 9.5 56 | 5.1 54 | 6.4 53 | 4.7 56 | 2.4 50 | 4.7 43 | 3.2 48 | 5.9 24 | 9.5 40 | 12.4 38 | 6.5 51 | 13.1 54 | 8.3 47 | 26.5 50 | 28.3 51 | 30 57 | 23.6 38 | 29.1 48 | 23.5 24 |
Learning Based Matting | 48 | 48.6 | 47.3 | 48.1 | 16 56 | 22 52 | 18.7 54 | 6.6 45 | 7.4 28 | 7.4 31 | 4.8 47 | 6.1 47 | 4.3 50 | 2.1 39 | 3.7 37 | 2.8 35 | 7.5 50 | 14.5 59 | 19.5 60 | 8.6 60 | 14.1 57 | 14.6 62 | 22.5 32 | 24.8 40 | 19.9 28 | 34.6 60 | 38.5 58 | 51.2 65 |
LMSPIR | 49.1 | 48.1 | 50.9 | 48.3 | 15.2 55 | 20 41 | 19.1 56 | 6.7 47 | 11.2 54 | 8.7 49 | 4.8 48 | 5.8 43 | 4.6 54 | 2.1 43 | 6.8 55 | 3 41 | 7.9 52 | 14.3 58 | 20.2 61 | 5.5 42 | 11.4 48 | 7 38 | 29.7 55 | 31 54 | 29.6 56 | 24 43 | 34.2 54 | 25 31 |
Shared Matting (Real Time) | 49.5 | 48.1 | 50.5 | 49.8 | 12.4 36 | 21.6 48 | 16.3 46 | 9.5 59 | 13.5 58 | 9.9 57 | 4.4 31 | 5.6 41 | 4.4 51 | 2.5 54 | 6.8 56 | 3.2 49 | 7.1 46 | 10.8 47 | 12.6 42 | 5.4 41 | 9.7 41 | 7.4 42 | 35.5 63 | 35.8 61 | 35.5 60 | 27.6 55 | 33.4 52 | 29.8 51 |
Closed-Form Matting | 50 | 48.6 | 47.6 | 53.9 | 12.7 41 | 21.9 51 | 17.2 52 | 5.9 33 | 8.5 39 | 8.6 48 | 4.7 44 | 6 46 | 4.3 46 | 2.2 44 | 4.6 41 | 3.3 50 | 9.3 58 | 12.1 54 | 19.3 59 | 8.3 57 | 14.9 59 | 13.4 61 | 34.2 60 | 32.4 57 | 27.4 52 | 26.5 52 | 25.7 34 | 48.3 63 |
Improving Sampling Criterion | 53.7 | 52.8 | 55.5 | 52.8 | 12.7 42 | 21.1 47 | 16.4 47 | 11.5 62 | 17 63 | 12.1 62 | 5.8 61 | 8.1 63 | 5.7 61 | 5.4 66 | 12.1 65 | 6.6 65 | 9.6 61 | 15.7 60 | 17.8 55 | 10.2 62 | 17 60 | 11.2 59 | 23.8 38 | 26.7 45 | 25.9 48 | 22.2 30 | 27.3 41 | 23.7 25 |
Cell-based matting Laplacian | 54.3 | 56.4 | 53 | 53.4 | 15.1 54 | 21.7 49 | 15.6 39 | 7.6 54 | 11.4 55 | 9 54 | 5.7 60 | 6.7 58 | 5.2 59 | 3.1 59 | 6.7 53 | 4.5 59 | 8.6 55 | 11.4 52 | 14.9 50 | 8.5 59 | 13.5 56 | 11.1 57 | 27.7 52 | 26.2 44 | 27.6 53 | 32.4 58 | 37.6 57 | 37.9 56 |
Large Kernel Matting | 54.5 | 56.1 | 53.9 | 53.5 | 17.2 58 | 21.8 50 | 20.7 60 | 7.2 52 | 9.6 49 | 8.4 44 | 5.3 56 | 6.6 56 | 4.6 53 | 2.9 57 | 8.2 60 | 4.2 57 | 8.6 56 | 12.1 55 | 14.7 49 | 8 55 | 13.4 55 | 11.2 58 | 33 58 | 31.8 56 | 26.1 49 | 32.1 57 | 32 50 | 38.4 58 |
Robust Matting | 55.2 | 51.5 | 56.1 | 58 | 17.3 59 | 28.4 62 | 21.1 61 | 10.1 60 | 16.9 62 | 11.4 60 | 4.8 46 | 6.5 55 | 5 58 | 2.8 56 | 7.3 57 | 4.4 58 | 7.3 47 | 14 57 | 18.1 56 | 6.8 52 | 14.6 58 | 10.6 56 | 22.7 33 | 26.1 43 | 32.1 58 | 34.4 59 | 37 55 | 38 57 |
SPS matting | 57.2 | 56.6 | 60.3 | 54.8 | 13.4 46 | 23 55 | 14.8 34 | 12.5 63 | 18.5 64 | 12.4 63 | 6.6 65 | 9.4 66 | 6.5 66 | 4.5 64 | 12.7 66 | 5.4 62 | 9.5 60 | 16.4 62 | 18.6 58 | 9.8 61 | 18.1 63 | 10.6 55 | 26.1 48 | 30.9 53 | 26.2 50 | 25 46 | 33.5 53 | 29.5 50 |
High-res matting | 58.2 | 56.8 | 59.5 | 58.4 | 18.6 61 | 25.8 59 | 24.6 63 | 8.6 57 | 14.1 59 | 11.1 59 | 5 52 | 6.2 50 | 4.8 57 | 2.5 52 | 8.3 61 | 3.2 47 | 7.8 51 | 14 56 | 21.4 62 | 8.5 58 | 18.1 64 | 12.2 60 | 35.3 62 | 38.1 63 | 42.6 64 | 38.7 61 | 54.6 64 | 36.8 55 |
Transfusive Weights | 58.6 | 58.8 | 58.5 | 58.5 | 23.2 63 | 26.7 61 | 22.4 62 | 9.2 58 | 11.9 57 | 10.4 58 | 6.1 63 | 8.1 62 | 5.9 63 | 3.8 62 | 8 59 | 5.3 61 | 12.9 64 | 21.4 64 | 37.6 68 | 13.1 67 | 22.8 66 | 18.3 67 | 20.9 28 | 22.9 34 | 19 23 | 59.5 65 | 65.7 65 | 72.8 66 |
Random Walk Matting | 61.9 | 63.5 | 59.5 | 62.8 | 17.9 60 | 20.3 43 | 19.4 57 | 11.3 61 | 15.6 60 | 11.8 61 | 5.8 62 | 7 59 | 6.3 65 | 3.4 60 | 6.7 54 | 4.6 60 | 13.1 66 | 22.1 65 | 27.4 64 | 12.3 66 | 18 62 | 15.7 65 | 44.1 67 | 43.5 66 | 41 63 | 75.1 66 | 81.8 67 | 80.6 67 |
Geodesic Matting | 63.1 | 64 | 62.9 | 62.5 | 26.9 66 | 38.5 67 | 32.5 66 | 14.2 64 | 16.5 61 | 17.4 65 | 11.7 68 | 14 69 | 9.4 68 | 7.6 68 | 15.1 67 | 8.7 68 | 12.8 63 | 16.7 63 | 15.1 51 | 7.3 54 | 12.1 51 | 9.8 53 | 37.3 65 | 37.4 62 | 42.8 65 | 48.6 64 | 50 63 | 48.6 64 |
Iterative BP Matting | 64.2 | 63.5 | 64.1 | 65 | 23.6 64 | 29.9 63 | 27.2 64 | 16.7 65 | 24.3 66 | 20.7 68 | 6.7 66 | 9 64 | 6.3 64 | 3.8 61 | 11.3 63 | 6.8 67 | 14.1 67 | 22.8 66 | 27.9 65 | 11.4 64 | 19 65 | 14.7 63 | 33.4 59 | 39.3 64 | 47.5 68 | 40.6 62 | 48.1 62 | 45.1 61 |
Easy Matting | 64.6 | 65 | 64 | 64.9 | 23.9 65 | 32.6 65 | 30 65 | 17.1 66 | 21.8 65 | 19.4 67 | 6.3 64 | 7.5 61 | 5.8 62 | 4.7 65 | 10.5 62 | 5.6 63 | 12.1 62 | 15.7 61 | 22.9 63 | 11.2 63 | 17 61 | 14.8 64 | 49.5 68 | 49.6 68 | 46.2 67 | 77.8 67 | 108.6 69 | 109.2 68 |
Improved Bayesian | 65.1 | 65 | 65.5 | 64.9 | 31.2 68 | 34.9 66 | 33.9 68 | 17.8 67 | 24.4 67 | 17.2 64 | 5.5 58 | 9.3 65 | 5.3 60 | 3.9 63 | 11.4 64 | 6.6 66 | 13 65 | 29.5 67 | 34.6 67 | 11.9 65 | 24.5 67 | 16.1 66 | 42.9 66 | 44.6 67 | 46 66 | 92.4 68 | 46.5 61 | 45.4 62 |
Bayesian Matting | 66.4 | 66.5 | 67.4 | 65.3 | 30.3 67 | 42.4 68 | 33.4 67 | 19.2 68 | 25.8 68 | 18.4 66 | 10.8 67 | 12.4 67 | 10.8 69 | 6.6 67 | 18.5 69 | 6.2 64 | 14.2 68 | 29.8 68 | 33.2 66 | 15.4 68 | 30.6 68 | 19.7 68 | 35.8 64 | 40.6 65 | 39.6 62 | 45.3 63 | 76.8 66 | 43.6 60 |
Poisson Matting | 68.8 | 69 | 68.6 | 68.8 | 51.8 69 | 56.2 69 | 52 69 | 28.3 69 | 43.5 69 | 30.7 69 | 12.1 69 | 13.7 68 | 9.2 67 | 11.7 69 | 18.4 68 | 11.2 69 | 22.4 69 | 36.8 69 | 55.5 69 | 21.4 69 | 32.2 69 | 22.7 69 | 53.6 69 | 72.9 69 | 58.4 69 | 125.5 69 | 84.8 68 | 139.7 69 |
Troll - Input image Drag the window to change the zoom. |
References
Method | Reference and notes | Implementation details |
Closed-Form Matting | A. Levin, D. Lischinski, Y. Weiss, A Closed Form Solution to Natural Image Matting, CVPR, 2006 | Matlab implementation on a Intel Core2 Quad with 2.4 GHZ |
Bayesian Matting | Y.Y. Chuang, B. Curless, D. Salesin, R. Szeliski, A Bayesian Approach to Digital Matting, CVPR, 2001 | C++ implementation on a Intel Core2 Quad with 2.4 GHZ |
Poisson Matting | J. Sun, J. Jia, C.K. Tang, H.Y. Shum, Poisson matting, SIGGRAPH, 2004 | Matlab implementation on a Intel Core2 Quad with 2.4 GHZ |
Easy Matting | Y. Guan, W. Cheny, X. Liang, Z. Ding, Q. Peng, Easy Matting: A Stroke Based Approach for Continuous Image Matting, Eurographics, 2006 | C++ implementation on a Intel Core2 Quad with 2.4 GHZ |
Random Walk Matting | L. Grady, T. Schiwietz, S. Aharon, Random Walks For Interactive Alpha-Matting, VIIP, 2005 | Matlab/C++ implementation on a Intel Core2 Quad with 2.4 GHZ |
Robust Matting | J. Wang, M. Cohen, Optimized Color Sampling for Robust Matting, CVPR, 2007 | C++ implementation on a Intel Core2 Quad with 2.4 GHZ |
Geodesic Matting | Xue Bai, Guillermo Sapiro, A geodesic framework for fast interactive image and video segmentation and matting, ICCV 2007 | C++ implementation on a Intel Core2 Duo with 2.53 GHZ |
Iterative BP Matting | Jue 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 matting | C. Rhemann, C. Rother, M. Gelautz, Improving Color Modeling for Alpha Matting. BMVC, 2008 | Matlab implementation on a Intel Core2 Duo with 2.4 GHZ |
High-res matting | C. Rhemann, C. Rother, A. Rav-Acha, M. Gelautz, T. Sharp, High ResolutionMatting via Interactive Trimap Segmentation. CVPR, 2008 | Matlab/C++ implementation on a Intel Core2 Duo with 2.4 GHZ |
Large Kernel Matting | Kaiming He, Jian Sun, and Xiaoou Tang, Fast Matting using Large Kernel Matting Laplacian Matrices, CVPR 2010 | C++ implementation on a Intel Core Duo with 2 GHZ |
Segmentation-based matting | Christoph Rhemann, Carsten Rother, Pushmeet Kohli, Margrit Gelautz, A Spatially Varying PSF-based Prior for Alpha Matting, CVPR 2010 | Matlab/C++ implementation on a Intel Core2 Quad with 2.39 GHZ |
Shared Matting | Eduardo S. L. Gastal and Manuel M. Oliveira, Shared Sampling for Real-Time Alpha Matting, Eurographics, 2010 | C++/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, 2010 | C++/GLSL implementation on a Core 2 Quad with 2.8 GHZ |
Learning Based Matting | Yuanjie Zheng, Chandra Kambhamettu, Yuanjie Zheng, Chandra Kambhamettu. Learning Based Digital Matting. ICCV 2009. SOURE CODE | Matlab/C++ implementation on a Intel Core2 Duo with 2.53 GHZ |
LMSPIR | Bei He, Guijin Wang, Zhiwei Ruan, Xuanwu Yin, Xiaokang Pei, Xinggang Lin, Local Matting based on Sample-pair Propagation and Iterative Refinement, ICIP 2012 | C++ implementation on a Intel Core2 Dual with 2 GHZ |
SVR Matting | Zhanpeng Zhang, Qingsong Zhu, Yaoqin Xie, Learning Based Alpha Matting using Support Vector Regression, ICIP 2012 | Matlab implementation on a Intel Pentium Dual-Core with 3 GHZ |
Cell-based matting Laplacian | Chen-Yu Tseng and Sheng-Jyh Wang, A cell-based matting Laplacian for contrast enhancement, ICIP 2012 | C++ implementation on a Intel Core i3 with 3 GHZ |
Global Sampling Matting | Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun, A Global Sampling Method for Alpha Matting, CVPR 2011 | C++ 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, 2010 | C++ implementation on a Intel Core2 with 3 GHZ |
Weighted Color and Texture Matting | E.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 Matting | Qifeng Chen, Dingzeyu Li, Chi-Keung Tang, KNN Matting, CVPR 2012 | Matlab implementation on a Intel Core 2 Duo with 2.13 GHZ |
SRLO Matting | Bei 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, 2013 | C++ implementation on a Intel Core2 Dual with 2 GHZ |
LNSP Matting | Xiaowu Chen, Dongqing Zou, Ping Tan, Image Matting with Local and Nonlocal Smooth Priors, CVPR 2013 | matlab implementation on a intel core2 with 2.2 GHZ |
CCM | Yongfang 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 Matting | Bei He, Guijin Wang, Chenbo Shi, Xuanwu Yin, Bo Liu, Xinggang Lin, Iterative Transductive Matting, ICIP 2013 | Matlab implementation on a Intel Core2 Dual with 2.2 GHZ |
Improving Sampling Criterion | Jun Cheng, Zhenjiang Miao, Improving Sampling Criterion for Alpha Matting, RACVPR2013 in Conjunction with ACPR2013 | C++ implementation on a Core i5 with 2.5 GHZ |
Transfusive Weights | Kaan Yucer, Alexander Sorkine-Hornung, and Olga Sorkine-Hornung, Transfusive Weights for Content-Aware Image Manipulation, VMV2013 | Matlab implementation on a Quad-Core Intel Xeon with 3.2 GHZ |
Comprehensive sampling | E.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 Texture | E.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 matting | Ahmad 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 Bayesian | Wenshuang Tan, Automatic Matting of Identification Photos, CAD/Graphics, 2013 | C++ implementation on a Intel Core(TM)i7-2600 with 3.4 GHZ |
Sparse coded matting | Jubin Johnson, Deepu Rajan, Hisham Cholakkal, Sparse Codes as Alpha Mattes, BMVC 2014. | Matlab implementation on a Intel Xeon with 3.2 GHZ |
LNCLM matting | B.-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 matting | Jubin 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 Sampling | Levent 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 |
LocalSamplingAndKnnClassification | Xiao 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 Matting | Donghyeon Cho, Yu-Wing Tai, Inso Kweon, Natural Image Matting using Deep Convolutional Neural Networks. ECCV 2016 | matlab implementation on a Intel Core i7 with 3.4 GHZ |
CSC Matting | Xiaoxue 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 Matting | Guangying 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 Matting | Ahmad 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 Matting | Ning Xu, Brian Price, Scott Cohen and Thomas Huang, Deep Image Matting, CVPR 2017 | Matlab implementation on a I7 with 2.7 GHZ |
Information-flow matting | Yagiz Aksoy, Tunc Ozan Aydin and Marc Pollefeys, Designing Effective Inter-Pixel Information Flow for Natural Image Matting, CVPR 2017 | Matlab implementation on a Intel Xeon with 3.5 GHZ |
ATPM Matting | Xiangyu Zhu, Ping Wang, Zhenghai Huang, Adaptive Propagation Matting Based on Transparency of Image, Multimedia Tools and Applications, vol. 77, pp. 9089-19112, 2018, doi: 10.1007/s11042-017-5357-7 | matlab implementation on a Intel Xeon with 2.4 GHZ |
Three-layer graph matting | Chao Li, Ping Wang, Xiangyu Zhu, Huali Pi, Three-layer graph framework with the sumD feature for alpha matting. Computer Vision and Image Understanding, vol. 162, pp. 34-45, 2017 | c++, octave implementation on a Intel Xeon with 3.2 GHZ |
Three Stages Matting | Xiao Chen, A Three-Stage Matting Method, IEEE Access, 5(99):27732-27739, 2017 | matlab implementation on a i7 6700k with 4 GHZ |
AlphaGAN | Sebastian Lutz, Konstantinos Amplianitis, Aljosa Smolic, AlphaGAN: Generative Adversarial Networks for Natural Image Matting, BMVC 2018 | Python implementation on a Intel i7-6700 with 3.4 GHZ |
SampleNet Matting | Jingwei Tang, Yagiz Aksoy, Cengiz Oztireli, Markus Gross, Tunc Ozan Aydin, Learning-based Sampling for Natural Image Matting, CVPR 2019 | Python implementation on a Intel Core i7-7700K with 4.7 GHZ |
VDRN Matting | Huan Tang, Yujie Huang, Ming'e Jing, Yibo Fan, Xiaoyang Zeng, Very deep residual network for image matting, IEEE ICIP 2019 | python implementation on a intel Xeon with 2.2 GHZ |
AdaMatting | Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, and Jian Sun, Disentangled Image Matting, ICCV 2019 | Python implementation on a Intel Core i7-7700K with 4.7 GHZ with 2.2 GHZ |
IndexNet Matting | Hao Lu, Yutong Dai, Chunhua Shen, Songcen Xu, Indices Matter: Learning to Index for Deep Image Matting, ICCV 2019 | Python (PyTorch) implementation on a Intel i7-8700, GTX1070 with 3.2 GHZ |
Context-aware Matting | Qiqi Hou, Feng Liu, Context-aware Image Matting for Simultaneous Foreground and Alpha Estimation. ICCV 2019 | Python, Tensorflow implementation on a 1080 Ti with 2.2 GHZ |
GCA Matting | Yaoyi Li, Hongtao Lu, Natural Image Matting via Guided Contextual Attention, AAAI 2020 | python implementation on a Intel(R) Xeon(R) CPU E5-2640, GeForce RTX 2080 Ti with 2.6 GHZ |
ATNet Matting | F. Zhou, Y. Tian, Z. Qi, Attention Transfer Network For Nature Image Matting, IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2020.3024213 | python implementation on a 1080Ti with 2.2 GHZ |
PIIAMatting | Yuhao Liu, Jiake Xie, Yu Qiao, Yong Tang, Xin Yang, Prior-Induced Information Alignment for Image Matting, IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3087007 | Python implementation on a GTX 2080ti with 2.6 GHZ |
SIM | Yanan Sun, Chi-Keung Tang, Yu-Wing Tai, Semantic Image Matting, CVPR 2021 | python implementation on a GTX 2080ti with 2.6 GHZ |
HDMatt | Haichao Yu, Ning Xu, Zilong Huang, Yuqian Zhou, Humphrey Shi, High-Resolution Deep Image Matting, AAAI 2021 | Python implementation on a Tesla V100 with 3.2 GHZ |
TIMI-Net | Yuhao Liu, Jiake Xie, Xiao Shi, Yu Qiao, Yujie Huang, Yong Tang, Xin Yang, Tripartite Information Mining and Integration for Image Matting, ICCV 2021 | python implementation on a tesla v100 with 3.5 GHZ |
A2U Matting | Yutong Dai, Hao Lu, Chunhua Shen, Learning Affinity-Aware Upsampling for Deep Image Matting, CVPR 2021 | Python implementation on a GTX 1080 Ti with 3.2 GHZ |
LFPNet | Qinglin Liu, Haozhe Xie, Shengping Zhang, Bineng Zhong, Rongrong Ji, Long-Range Feature Propagating for Natural Image Matting, ACM MM 2021 | Python implementation on a Nvidia GTX 1080Ti with 1.5 GHZ |
IamAlpha | Avinav Goel, Manoj Kumar, Pavan Sudheendra, IamAlpha: Instant and Adaptive Mobile Network for Alpha Matting, BMVC 2021 | Python implementation on a Nvidia Tesla P40 with 3.1 GHZ |
TMFNet | anonymous, Trimap-guided Feature Mining and Fusion Network for Natural Image Matting, submission to CVIU, 2022 | python implementation on a Tesla V100 with 3.5 GHZ |
FGI Matting | Hang Cheng, Shugong Xu, Xiufeng Jiang, Rongrong Wang, Deep Image Matting with Flexible Guidance Input, BMVC 2021 | python implementation on a RTX 2080 Ti with 2.6 GHZ |
LSA Matting | Rui Wang, Jun Xie, Jiacheng Han, Dezhen Qi, Improving Deep Image Matting via Local Smoothness Assumption, IEEE ICME 2022 | Python implementation on a Intel Xeon with 2.3 GHZ |
RMat | Yutong Dai, Brian Price, He Zhang, Chunhua Shen, Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation, CVPR 2022 | python implementation on a Tesla V100 with 3.2 GHZ |
TransMatting: Enhancing Transparent ... | Huanqia Cai, Fanglei Xue, Lele Xu, Lili Guo, TransMatting: Enhancing Transparent Objects Matting with Transformers, ECCV 2022, accepted | Python implementation on a Intel(R) Xeon(R) Silver 4210R with 2.4 GHZ |
LiteMatting | anonymous, Lightweight Image Matting via Efficient Non-Local Guidance, anonymous submission 2022 | python implementation on a Intel with 3.8 GHZ |
CDI-Net | Zhiwei Ma, Guilin Yao, submission to Journal of Visual Communication and Image Representation, 2022 | Python implementation on a AMD Ryzen 9 5900HX with 3.3 GHZ |