Prototype learning and collaborative representation using Grassmann manifolds for image set classification
作者：
Highlights：
• Principle component and variation subspaces are constructed for an over-complete dictionary.
• A novel prototype and variation model (P+V) based collaborative representation for Grassmann manifolds is proposed to deal with image set classification naturally.
• Previous special matrices are generalized to common sparse matrices.
• Experimental results show the superiorities of our methods.
摘要
•Principle component and variation subspaces are constructed for an over-complete dictionary.•A novel prototype and variation model (P+V) based collaborative representation for Grassmann manifolds is proposed to deal with image set classification naturally.•Previous special matrices are generalized to common sparse matrices.•Experimental results show the superiorities of our methods.
论文关键词：Image set classification,Collaborative representation,Prototype learning,Grassmann manifolds
论文评审过程：Received 18 March 2019, Revised 12 September 2019, Accepted 19 November 2019, Available online 20 November 2019, Version of Record 25 November 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107123
