Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2021
Abstract
Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles. Drawing a fine trimap requires a large amount of user effort, while using scribbles can hardly obtain satisfactory alpha mattes for non-professional users. Some recent deep learning-based matting networks rely on large-scale composite datasets for training to improve performance, resulting in the occasional appearance of obvious artifacts when processing natural images. In this article, we explore the intrinsic relationship between user input and alpha mattes and strike a balance between user effort and the quality of alpha mattes. In particular, we propose an interactive framework, referred to as smart scribbles, to guide users to draw few scribbles on the input images to produce high-quality alpha mattes. It first infers the most informative regions of an image for drawing scribbles to indicate different categories (foreground, background, or unknown) and then spreads these scribbles (i.e., the category labels) to the rest of the image via our well-designed two-phase propagation. Both neighboring low-level affinities and high-level semantic features are considered during the propagation process. Our method can be optimized without large-scale matting datasets and exhibits more universality in real situations. Extensive experiments demonstrate that smart scribbles can produce more accurate alpha mattes with reduced additional input, compared to the state-of-the-art matting methods.
Keywords
Image matting, alpha matte, markov chain, deep learning, label propagation
Discipline
Graphics and Human Computer Interfaces
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Transactions on Multimedia Computing, Communications and Applications
Volume
16
Issue
4
First Page
1
Last Page
21
ISSN
1551-6857
Identifier
10.1145/3408323
Publisher
Association for Computing Machinery (ACM)
Citation
XIN, Yang; QIAO, Yu; CHEN, Shaozhe; HE, Shengfeng; YIN, Baocai; ZHANG, Qiang; WEI, Xiaopeng; and LAU, Rynson W. H..
Smart scribbles for image matting. (2021). ACM Transactions on Multimedia Computing, Communications and Applications. 16, (4), 1-21.
Available at: https://ink.library.smu.edu.sg/sis_research/7881
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3408323