Abstract：In order to improve the accuracy of the video semantic analysis, the video semantic analysis was proposed based on nonlinear identifiable sparse representation. Kernel function and category vector were introduced into the K-SVD dictionary optimization for the sparse representation. The sparse representation features were mapped into a high dimensional space to establish label identifiable model by Fisher criterion. An optimization dictionary was generated according to the constraint of the proposed model. The sparse representation codes of the video features were calculated by the dictionary. An identification criterion was proposed for the classification of the video sparse representation features to analyze the video semantic by the criterion. The video semantic concept was analyzed in the news video library of TRECVID 2007. The experimental results show that the discriminability of the sparse representation of video features can be markedly improved with improved accuracy of video semantic analysis.
2013, Vol. 34 
