Abstract: Video semantic analysis has been a research hotspot.Traditional sparse representation methods cannot produce similar coding result when the input video features are close to each other.We assumed that similar video features should be encoded as similar sparse codes in the process of video semantic analysis based on sparse representation.In other words,the similar video features should have smaller distance between their sparse codes.In order to improve the accuracy of video semantic analysis,locality-sensitive discriminant sparse representation(LSDSR) based on the hypothesis for video semantic analysis was developed.In proposed method,discriminant loss function based on sparse coefficient is introduced into the locality-sensitive sparse representation.An optimization dictionary is generated with the constraint.In the process,the sparse coding coefficients have both small within-class scatter and large between-class scatter using Fisher criterion,so as to build the discriminant sparse model in the LSDSR.The proposed method was extensively evaluated on related video databases in comparison with existing sparse representation methods.The experimental results show that this method significantly enhances the power of discrimination of sparse representation features,and consequently improves the accuracy of video semantic analysis.

Key words: Video semantic,Sparse representation,Locality-sensitive,Discriminant