# Sparse Coding for Alpha Matting

arXiv: https://arxiv.org/abs/1604.02898 · HTML: https://mlsanity.com/paper/1604.02898/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 6 of 6)

- [MODNet-V: Improving Portrait Video Matting via Background Restoration](https://mlsanity.com/paper/2109.11818/) (2021)
- [EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting](https://mlsanity.com/paper/2308.12831/) (2023)
- [Alpha Matte Generation from Single Input for Portrait Matting](https://mlsanity.com/paper/2106.03210/) (2021)
- [Adaptive Background Matting Using Background Matching](https://mlsanity.com/paper/2203.05193/) (2022)
- [Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors](https://mlsanity.com/paper/2501.16147/) (2025)
- [HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence](https://mlsanity.com/paper/2608.26517/) (2026)

## Abstract (arXiv)

Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F,B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms current state-of-the-art in image and video matting.