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Linear Span Network for Object Skeleton Detection

  • Chang Liu
  • Wei Ke
  • Fei Qin
  • Qixiang YeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11206)

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.

Keywords

Linear span framework Linear span unit Linear span network Skeleton detection 

Notes

Acknowledgement

This work was partially supported by the National Nature Science Foundation of China under Grant 61671427 and Grant 61771447, and Beijing Municipal Science & Technology Commission under Grant Z181100008918014.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina

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