  Object skeleton is a useful cue for object detection, complementary to the
object contour, as it provides a structural representation to describe the
relationship among object parts. While object skeleton extraction in natural
images is a very challenging problem, as it requires the extractor to be able
to capture both local and global image context to determine the intrinsic scale
of each skeleton pixel. Existing methods rely on per-pixel based multi-scale
feature computation, which results in difficult modeling and high time
consumption. In this paper, we present a fully convolutional network with
multiple scale-associated side outputs to address this problem. By observing
the relationship between the receptive field sizes of the sequential stages in
the network and the skeleton scales they can capture, we introduce a
scale-associated side output to each stage. We impose supervision to different
stages by guiding the scale-associated side outputs toward groundtruth
skeletons of different scales. The responses of the multiple scale-associated
side outputs are then fused in a scale-specific way to localize skeleton pixels
with multiple scales effectively. Our method achieves promising results on two
skeleton extraction datasets, and significantly outperforms other competitors.
