Automatic Trimap Generation for Image Matting
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2017.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 2.
6 papers.
- 2021Salient Image Mattingcited by 6
- 2021Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attentioncited by 1
- 2022SGM-Net: Semantic Guided Matting Netcited by 1
- 2023Color-aware Deep Temporal Backdrop Duplex Matting Systemcited by 1
- 2022Attention based Memory video portrait mattingcited by 0
- 2023High-Quality Face Caricature via Style Translationcited by 0
- 2023High-Quality Face Caricature via Style Translation22 Nov 2023
- 2023Color-aware Deep Temporal Backdrop Duplex Matting System5 Jun 2023
- 2022SGM-Net: Semantic Guided Matting Net16 Aug 2022
- 2022Attention based Memory video portrait matting14 Mar 2022
- 2021Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention31 Mar 2021
- 2021Salient Image Matting23 Mar 2021