  Image matting aims to obtain an alpha matte that separates foreground objects
from the background accurately. Recently, trimap-free matting has been well
studied because it requires only the original image without any extra input.
Such methods usually extract a rough foreground by itself to take place trimap
as further guidance. However, the definition of 'foreground' lacks a unified
standard and thus ambiguities arise. Besides, the extracted foreground is
sometimes incomplete due to inadequate network design. Most importantly, there
is not a large-scale real-world matting dataset, and current trimap-free
methods trained with synthetic images suffer from large domain shift problems
in practice. In this paper, we define the salient object as foreground, which
is consistent with human cognition and annotations of the current matting
dataset. Meanwhile, data and technologies in salient object detection can be
transferred to matting in a breeze. To obtain a more accurate and complete
alpha matte, we propose a network called \textbf{M}ulti-\textbf{F}eature
fusion-based \textbf{C}oarse-to-fine Network \textbf{(MFC-Net)}, which fully
integrates multiple features for an accurate and complete alpha matte.
Furthermore, we introduce image harmony in data composition to bridge the gap
between synthetic and real images. More importantly, we establish the largest
general matting dataset \textbf{(Real-19k)} in the real world to date.
Experiments show that our method is significantly effective on both synthetic
and real-world images, and the performance in the real-world dataset is far
better than existing matting-free methods. Our code and data will be released
soon.
