  Image segmentation needs both local boundary position information and global
object context information. The performance of the recent state-of-the-art
method, fully convolutional networks, reaches a bottleneck due to the neural
network limit after balancing between the two types of information
simultaneously in an end-to-end training style. To overcome this problem, we
divide the semantic image segmentation into temporal subtasks. First, we find a
possible pixel position of some object boundary; then trace the boundary at
steps within a limited length until the whole object is outlined. We present
the first deep reinforcement learning approach to semantic image segmentation,
called DeepOutline, which outperforms other algorithms in Coco detection
leaderboard in the middle and large size person category in Coco val2017
dataset. Meanwhile, it provides an insight into a divide and conquer way by
reinforcement learning on computer vision problems.
