Structure-Constrained Low-Rank and Partial Sparse Representation with Sample Selection for image classification

作者：

Highlights：

• We propose a Structure-Constrained Low-Rank Dictionary Learning algorithm and develop its optimization strategy.

• We propose a Low-Rank and Partial Sparse Representation algorithm and develop its optimization strategy.

• We prove that the solution to LRPSR is block sparse for independent subdictionaries.

• We design a Sample Selection procudure to accelerate LRPSR.

• Experimental results show that our proposed method outperforms most sparse or low-rank based image classification algorithms proposed recently.

摘要

Highlights•We propose a Structure-Constrained Low-Rank Dictionary Learning algorithm and develop its optimization strategy.•We propose a Low-Rank and Partial Sparse Representation algorithm and develop its optimization strategy.•We prove that the solution to LRPSR is block sparse for independent subdictionaries.•We design a Sample Selection procudure to accelerate LRPSR.•Experimental results show that our proposed method outperforms most sparse or low-rank based image classification algorithms proposed recently.

论文关键词：Sparse coding,Low-rank,Dictionary learning,Image classification,Structured sparsity

论文评审过程：Received 28 July 2015, Revised 22 January 2016, Accepted 23 January 2016, Available online 3 February 2016, Version of Record 23 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.01.026