Alpha Matting Algorithm Performance
Performance comparison of alpha matting algorithms across different metrics. Lower values are better for all metrics.
Data sourced from alphamatting.com, a comprehensive benchmark for alpha matting algorithms.
Evaluation Metrics for Image Matting#
The paper "A Perceptually Motivated Online Benchmark for Image Matting" introduces four evaluation metrics for assessing image matting quality. They are:
1. Sum of Absolute Differences (SAD)#
- Traditional metric that computes the pixel-wise absolute error between the estimated alpha matte and the ground truth.
- Measures the overall deviation but is not always correlated with perceived visual quality.
2. Mean Squared Error (MSE)#
- Another pixel-based metric that calculates the average squared difference between predicted and ground truth alpha values.
- Like SAD, it may fail to reflect visual artifacts (e.g., ConnectivityError issues).
3. Gradient Error#
- A perceptually motivated metric focusing on the preservation of alpha matte gradients (important for fine details like hair or edges).
- It measures the difference between the gradients of the estimated and ground truth mattes:
- Shown to correlate better with human perception of smoothness and edge quality.
4. ConnectivityError#
- Designed to penalize disconnects or fragmentation of the foreground object.
- It evaluates how well the ConnectivityError of regions in the alpha matte matches the ground truth using a ConnectivityError function .
- Particularly important for assessing structural integrity (e.g., connected hair strands).
Key Findings:#
- SAD and MSE have weak correlation with human judgment (correlation ~0.28–0.51).
- Gradient and ConnectivityError measures correlate much better (correlation ~0.75 with human ratings).
- Different algorithms rank differently depending on the metric (e.g., Random Walk matting excels in ConnectivityError, Closed-form matting scores best for SAD/MSE/Gradient).
Algorithm / Model | SAD | MSE | Gradient | Connectivity |
---|---|---|---|---|
TransMatting | 3.1 | 2.3 | 2.8 | 6.2 |
TMFNet | 3.4 | 4.0 | 3.9 | 15.0 |
IamAlpha | 4.4 | 4.8 | 4.8 | 12.8 |
LFPNet | 4.7 | 4.1 | 2.8 | 11.2 |
TIMI-Net | 4.9 | 5.5 | 6.2 | 8.8 |
SIM | 5.8 | 6.3 | 6.2 | 9.6 |
PIIAMatting | 9.1 | 11.3 | 11.8 | 17.7 |
HDMatt | 10.5 | 10.3 | 8.7 | 29.9 |
AdaMatting | 12.6 | 12.8 | 12.3 | 22.3 |
A2U Matting | 12.8 | 14.6 | 11.3 | 26.3 |
SampleNet Matting | 13.0 | 13.2 | 14.2 | 25.4 |
FGI Matting | 13.1 | N/A | N/A | N/A |
GCA Matting | 14.2 | 14.5 | 12.8 | 21.7 |
ATNet Matting | 15.4 | 14.9 | 18.1 | 24.9 |
VDRN Matting | 15.8 | 17.4 | 18.7 | 22.4 |
Deep Matting | 16.1 | 18.1 | 22.5 | 20.0 |
Information-flow matting | 17.9 | 18.7 | 25.3 | 30.2 |
IndexNet Matting | 19.3 | 22.0 | 17.6 | 24.7 |
DCNN Matting | 20.0 | 19.2 | 23.7 | 26.7 |
AlphaGAN | 20.9 | 23.2 | 22.4 | 35.8 |
Context-aware Matting | 22.8 | 16.7 | 13.7 | 25.2 |
Three-layer graph matting | 25.0 | 24.4 | 27.3 | 26.3 |
ATPM Matting | 27.3 | 29.4 | 31.5 | 34.0 |
Three Stages Matting | 28.5 | 25.7 | 31.4 | 25.9 |
CSC Matting | 30.0 | 34.1 | 34.8 | 37.1 |
LNSP Matting | 30.1 | 26.3 | 28.9 | 19.4 |
Graph-based sparse matting | 31.1 | 30.7 | 28.1 | 40.0 |
KL-Divergence Based Sparse Sampling | 31.2 | 29.8 | 28.2 | 40.0 |
Patch-based Matting | 31.3 | 28.1 | 28.1 | 33.2 |
TSPS-RV Matting | 32.6 | 31.6 | 35.4 | 31.5 |
Iterative Transductive Matting | 33.2 | 37.1 | 41.3 | 50.0 |
SVR Matting | 33.8 | 30.4 | 32.3 | 26.5 |
Comprehensive sampling | 33.8 | 31.4 | 29.4 | 42.8 |
Comprehensive Weighted Color and Texture | 34.3 | 32.2 | 34.4 | 37.8 |
Sparse coded matting | 34.8 | 34.7 | 31.8 | 38.5 |
LocalSamplingAndKnnClassification | 36.3 | 34.5 | 38.7 | 27.8 |
Weighted Color and Texture Matting | 37.0 | 36.4 | 40.4 | 38.4 |
CCM | 37.3 | 30.3 | 32.5 | 18.7 |
LNCLM matting | 37.3 | 37.1 | 39.7 | 37.4 |
Shared Matting | 37.5 | 37.5 | 33.5 | 37.7 |
Global Sampling Matting | 39.8 | 36.3 | 32.8 | 34.5 |
SRLO Matting | 40.8 | 42.5 | 42.6 | 46.3 |
Segmentation-based matting | 41.1 | 40.4 | 32.6 | 34.3 |
Improved color matting | 42.3 | 40.8 | 33.3 | 27.6 |
KNN Matting | 42.6 | 37.5 | 44.3 | 33.9 |
Local Spline Regression (LSR) | 42.7 | 44.0 | 44.2 | 28.9 |
Global Sampling Matting (filter version) | 42.9 | 44.5 | 38.5 | 46.8 |
Learning Based Matting | 44.0 | 43.7 | 43.3 | 30.8 |
LMSPIR | 45.2 | 44.9 | 43.5 | 43.2 |
Shared Matting (Real Time) | 45.6 | 47.0 | 48.5 | 45.5 |
Closed-Form Matting | 46.0 | 44.6 | 44.1 | 19.2 |
Improving Sampling Criterion | 49.7 | 41.6 | 42.5 | 34.2 |
Cell-based matting Laplacian | 50.3 | 49.8 | 51.0 | 29.0 |
Large Kernel Matting | 50.5 | 48.2 | 46.3 | 25.6 |
Robust Matting | 51.2 | 49.6 | 45.3 | 45.0 |
SPS matting | 53.3 | 44.5 | 40.7 | 42.1 |
High-res matting | 54.2 | 51.5 | 46.9 | 35.3 |
Transfusive Weights | 54.7 | 51.3 | 54.2 | 12.8 |
Random Walk Matting | 57.9 | 57.4 | 55.5 | 8.7 |
Geodesic Matting | 59.1 | N/A | N/A | 52.2 |
Iterative BP Matting | 60.2 | N/A | N/A | 52.5 |
Easy Matting | 60.6 | N/A | N/A | N/A |
Improved Bayesian | 61.1 | N/A | N/A | N/A |
Bayesian Matting | 62.4 | N/A | N/A | N/A |
Poisson Matting | 64.8 | N/A | N/A | N/A |
Showing 65 algorithms with their performance metrics.
Metrics: SAD (Sum of Absolute Differences), MSE (Mean Squared Error), Gradient (Gradient Error), Connectivity (Connectivity Error). Lower values indicate better performance.
Color coding: Values are colored from green (best) to red (worst) within each column, with yellow and orange representing intermediate performance levels.