Globally consistent correspondence of multiple feature sets using proximal Gauss–Seidel relaxation
作者：
Highlights：
• We study finding globally consistent correspondence from multiple feature sets.
• A novel method is proposed based on Proximal Gauss–Seidel Relaxation (PGSR).
• PGSR is more robust to noise, outliers and deformation than competitors.
• The optimization has general convergence guarantee.
• The scale of our formulation is linear w.r.t. the number of feature sets.
摘要
Highlights•We study finding globally consistent correspondence from multiple feature sets.•A novel method is proposed based on Proximal Gauss–Seidel Relaxation (PGSR).•PGSR is more robust to noise, outliers and deformation than competitors.•The optimization has general convergence guarantee.•The scale of our formulation is linear w.r.t. the number of feature sets.
论文关键词：Feature correspondence,Multiple feature set correspondence,Permutation matrix,Convex relaxation,Proximal Gauss–Seidel method,Graph matching
论文评审过程：Received 22 November 2014, Revised 23 July 2015, Accepted 24 September 2015, Available online 9 October 2015, Version of Record 27 November 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.029
