Discriminative self-adapted locality-sensitive sparse representation for video semantic analysis

Abstract

In recent years, sparse representation has attracted a blooming interest in the areas of pattern recognition, image processing, and computer vision. In video semantic analysis, the diversity of scene for the same semantic content in video always exists. Using dictionary learning in sparse representation can capture the latent relationship among the original diverse video semantic features. To enhance the discriminative ability of diverse video semantic features, the method of discriminative self-adapted locality-sensitive sparse representation for video semantic analysis is proposed. In the proposed method, a discriminative self-adaptive locality-sensitive dictionary learning method (DSALSDL) is designed. In DSALSDL, a self-adaptive local adapter is built to join in the process of dictionary learning for sparse representation, so as to obtain the potential information of the video data. Furthermore, in the self-adaptive locality-sensitive sparse representation, a discriminant loss function based on class-specific representation coefficients is imposed to further learn appropriate dictionary for video semantic analysis. Using the self-adaptive local adapter and discriminant loss function in dictionary learning, the sparse representation is exploited for video semantic concept detection. The proposed method is evaluated on the related video databases in comparison with existing relative sparse representation methods. Experimental results show that our method can improve the power of discrimination of video features and improve the accuracy of video semantic concept detection.