Video semantic analysis based kernel locality-sensitive discriminative sparse representation

Abstract

Kernel based locality-sensitive sparse representation is currently an active hot research topic in artificial intelligence, pattern recognition, signal processing and multimedia applications. In this paper, a new kernel based approach; named Video Semantic Analysis based Kernel Locality-Sensitive Discriminative Sparse Representation (KLSDSR) is proposed. This is to improve video semantic analysis for military intelligence systems and video surveillance. The proposed algorithm is able to learn more discriminative sparse representation (SR) coefficients based on group sparsity for video semantic analysis by incorporating both sparsity and locality-sensitive in kernel feature space with mapping of the SR features into a high dimensional space. Furthermore, an optimal dictionary is however engendered to compute the SR of video features aimed at good preservation of the locality structure among video semantic features and an improvement on computational cost. The proposed method gave promising classification results, when compared with state-of-the-art comparative approaches based on experimental results on video semantic concepts, which significantly improves the discrimination of SR features and it outperformed the other baseline methods.