Linear Span Network for Object Skeleton Detection
This work addresses robust skeleton detection for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles object skeleton detection by proposing a Linear Span Network (LSN) that minimizes reconstruction error and increases feature independence, achieving state-of-the-art performance as validated by experiments.
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.