  Natural image matting separates the foreground from background in fractional
occupancy which can be caused by highly transparent objects, complex foreground
(e.g., net or tree), and/or objects containing very fine details (e.g., hairs).
Although conventional matting formulation can be applied to all of the above
cases, no previous work has attempted to reason the underlying causes of
matting due to various foreground semantics.
  We show how to obtain better alpha mattes by incorporating into our framework
semantic classification of matting regions. Specifically, we consider and learn
20 classes of matting patterns, and propose to extend the conventional trimap
to semantic trimap. The proposed semantic trimap can be obtained automatically
through patch structure analysis within trimap regions. Meanwhile, we learn a
multi-class discriminator to regularize the alpha prediction at semantic level,
and content-sensitive weights to balance different regularization losses.
Experiments on multiple benchmarks show that our method outperforms other
methods and has achieved the most competitive state-of-the-art performance.
Finally, we contribute a large-scale Semantic Image Matting Dataset with
careful consideration of data balancing across different semantic classes.
