  Image matting is an important vision problem. The main stream methods for it
combine sampling-based methods and propagation-based methods. In this paper, we
deal with the combination with a normalized weighting parameter, which could
well control the relative relationship between information from sampling and
from propagation. A reasonable value range for this parameter is given based on
statistics from the standard benchmark dataset. The matting is further improved
by introducing semi-supervised learning iterations, which automatically refine
the trimap without user's interaction. This is especially beneficial when the
trimap is coarse. The experimental results on standard benchmark dataset have
shown that both the normalized weighting parameter and the semi-supervised
learning iteration could significantly improve the matting performance.
