  Object skeletons are useful for object representation and object detection.
They are complementary to the object contour, and provide extra information,
such as how object scale (thickness) varies among object parts. But object
skeleton extraction from natural images is very challenging, because it
requires the extractor to be able to capture both local and non-local image
context in order to determine the scale of each skeleton pixel. In this paper,
we present a novel fully convolutional network with multiple scale-associated
side outputs to address this problem. By observing the relationship between the
receptive field sizes of the different layers in the network and the skeleton
scales they can capture, we introduce two scale-associated side outputs to each
stage of the network. The network is trained by multi-task learning, where one
task is skeleton localization to classify whether a pixel is a skeleton pixel
or not, and the other is skeleton scale prediction to regress the scale of each
skeleton pixel. Supervision is imposed at different stages by guiding the
scale-associated side outputs toward the groundtruth skeletons at the
appropriate scales. The responses of the multiple scale-associated side outputs
are then fused in a scale-specific way to detect skeleton pixels using multiple
scales effectively. Our method achieves promising results on two skeleton
extraction datasets, and significantly outperforms other competitors.
Additionally, the usefulness of the obtained skeletons and scales (thickness)
are verified on two object detection applications: Foreground object
segmentation and object proposal detection.
