  Image matting is an ill-posed problem that usually requires additional user
input, such as trimaps or scribbles. Drawing a fne 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 frst
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
afnities 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.
