Abstract. 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) which introduces Linear Span Units (LSUs)
to minimizes the reconstruction error. LSN further utilizes subspace linear span besides the feature linear span to increase the independence of
convolutional features and the efficiency of feature integration, which enhances 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