Trimap generation with background for natural image matting

Qian Fu; Yihui Liang; Zou Kun; Fujian Feng; Xiang Xu

doi:10.1117/12.3031586

18 July 2024 Trimap generation with background for natural image matting

Qian Fu, Yihui Liang, Zou Kun, Fujian Feng, Xiang Xu

Proceedings Volume 13179, International Conference on Optics and Machine Vision (ICOMV 2024); 131791I (2024) https://doi.org/10.1117/12.3031586
Event: International Conference on Optics and Machine Vision (ICOMV 2024), 2024, Nanchang, China

Abstract

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TPBG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Fu, Yihui Liang, Zou Kun, Fujian Feng, and Xiang Xu "Trimap generation with background for natural image matting", Proc. SPIE 13179, International Conference on Optics and Machine Vision (ICOMV 2024), 131791I (18 July 2024); https://doi.org/10.1117/12.3031586

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