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.

本文提出使用语义分类的前景语境信息以及语义 trimap 对自然图像抠图进行改进，并就此进行了大规模实验，获得了当下具有最高竞争力的性能表现。同时，我们提供了一个考虑不同语义类别平衡的大型语义图像抠图数据集。