Bilinear low-rank coding framework and extension for robust image recovery and feature representation
作者：
Highlights：
• We mainly explore the low-rank image recovery problem.
• A bilinear low-rank image coding framework is proposed.
• For recovery, TLRR preserves both column and row information of given data.
• Out-of-sample extension of TLRR is presented for handling outside data.
• We propose two local and global low-rank subspace learning methods for feature learning.
摘要
•We mainly explore the low-rank image recovery problem.•A bilinear low-rank image coding framework is proposed.•For recovery, TLRR preserves both column and row information of given data.•Out-of-sample extension of TLRR is presented for handling outside data.•We propose two local and global low-rank subspace learning methods for feature learning.
论文关键词：Image recovery,Bilinear low-rank coding,Image representation,Subspace learning,Out-of-sample extension
论文评审过程：Received 9 May 2014, Revised 1 May 2015, Accepted 2 June 2015, Available online 9 June 2015, Version of Record 31 July 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.06.001
