  Robust object skeleton detection requires to explore rich representative
visual features and effective feature fusion strategies. In this paper, we
first re-visit the implementation of HED, the essential principle of which can
be ideally described with a linear reconstruction model. Hinted by this, we
formalize a Linear Span framework, and propose Linear Span Network (LSN)
modified by Linear Span Units (LSUs), which minimize the reconstruction error
of convolutional network. LSN further utilizes subspace linear span beside the
feature linear span to increase the independence of convolutional features and
the efficiency of feature integration, which enlarges the capability of fitting
complex ground-truth. As a result, LSN can effectively suppress the cluttered
backgrounds and reconstruct object skeletons. Experimental results validate the
state-of-the-art performance of the proposed LSN.
