# Automatic Trimap Generation for Image Matting

arXiv: https://arxiv.org/abs/1707.00333 · HTML: https://mlsanity.com/paper/1707.00333/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2017.

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

- [Salient Image Matting](https://mlsanity.com/paper/2103.12337/) (2021)
- [Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention](https://mlsanity.com/paper/2103.17020/) (2021)
- [SGM-Net: Semantic Guided Matting Net](https://mlsanity.com/paper/2208.07496/) (2022)
- [Color-aware Deep Temporal Backdrop Duplex Matting System](https://mlsanity.com/paper/2306.02954/) (2023)
- [Attention based Memory video portrait matting](https://mlsanity.com/paper/2203.06890/) (2022)
- [High-Quality Face Caricature via Style Translation](https://mlsanity.com/paper/2311.13338/) (2023)

## Abstract (arXiv)

Image matting is a longstanding problem in computational photography. Although, it has been studied for more than two decades, yet there is a challenge of developing an automatic matting algorithm which does not require any human efforts. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte form the input image. In this paper, we present a simple and efficient approach to automatically generate the trimap from the input image and make the whole matting process free from human-in-the-loop. We use learning based matting method to generate the matte from the automatically generated trimap. Experimental results demonstrate that our method produces good quality trimap which results into accurate matte estimation. We validate our results by replacing the automatically generated trimap by manually created trimap while using the same image matting algorithm.