  Compared to the general semantic segmentation problem, portrait segmentation
has higher precision requirement on boundary area. However, this problem has
not been well studied in previous works. In this paper, we propose a
boundary-sensitive deep neural network (BSN) for portrait segmentation. BSN
introduces three novel techniques. First, an individual boundary-sensitive
kernel is proposed by dilating the contour line and assigning the boundary
pixels with multi-class labels. Second, a global boundary-sensitive kernel is
employed as a position sensitive prior to further constrain the overall shape
of the segmentation map. Third, we train a boundary-sensitive attribute
classifier jointly with the segmentation network to reinforce the network with
semantic boundary shape information. We have evaluated BSN on the current
largest public portrait segmentation dataset, i.e, the PFCN dataset, as well as
the portrait images collected from other three popular image segmentation
datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior
quantitative and qualitative performance over state-of-the-arts on all the
datasets, especially on the boundary area.
