Video semantic analysis based kernel locality-sensitive discriminative sparse representation
作者：
Highlights：
• Efficient reconstructed dictionary to enforce dependencies in video features.
• High recognition performance by exploring nonlinear discriminative information.
• Encoding of video features into high dimensional feature space.
• Optimized sparse representation coefficient is generated for classification.
• Minimum reconstruction error is determined and the test sample classified.
摘要
•Efficient reconstructed dictionary to enforce dependencies in video features.•High recognition performance by exploring nonlinear discriminative information.•Encoding of video features into high dimensional feature space.•Optimized sparse representation coefficient is generated for classification.•Minimum reconstruction error is determined and the test sample classified.
论文关键词：Kernel sparsity,Sparse representation,Locality information,Dictionary learning,Video Semantic Analysis,Group Sparsity
论文评审过程：Received 27 July 2018, Revised 17 October 2018, Accepted 8 November 2018, Available online 9 November 2018, Version of Record 14 November 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.11.016
