  Image matting is a fundamental and challenging problem in computer vision and
graphics. Most existing matting methods leverage a user-supplied trimap as an
auxiliary input to produce good alpha matte. However, obtaining high-quality
trimap itself is arduous, thus restricting the application of these methods.
Recently, some trimap-free methods have emerged, however, the matting quality
is still far behind the trimap-based methods. The main reason is that, without
the trimap guidance in some cases, the target network is ambiguous about which
is the foreground target. In fact, choosing the foreground is a subjective
procedure and depends on the user's intention. To this end, this paper proposes
an improved deep image matting framework which is trimap-free and only needs
several user click interactions to eliminate the ambiguity. Moreover, we
introduce a new uncertainty estimation module that can predict which parts need
polishing and a following local refinement module. Based on the computation
budget, users can choose how many local parts to improve with the uncertainty
guidance. Quantitative and qualitative results show that our method performs
better than existing trimap-free methods and comparably to state-of-the-art
trimap-based methods with minimal user effort.
