A Video Semantic Analysis Method Based on Kernel Discriminative Sparse Representation and Weighted KNN

To improve the video semantic analysis for video surveillance, a new video semantic analysis method based on the kernel discriminative sparse representation (KSVD) and weighted K nearest neighbors (KNN) is proposed in this paper. A discriminative model is built by introducing a kernel discriminative function to the KSVD dictionary optimization algorithm, mapping the sparse representation features into a high-dimensional space. The optimal dictionary is then generated and applied to compute the sparse representations of video features. For video semantic analysis, a weighted KNN algorithm based on the optimal sparse representation is proposed. In the algorithm, a kernel function is introduced to establish discrimination about sparse representation features and the classification vote result is weighted, the purpose of which is to improve the accuracy and rationality for video semantic analysis. The experimental results show that the proposed method significantly improves the discrimination of sparse representation features when compared with the traditional KSVD-based support vector machine method. The method can effectively detect the concept and event, which can be potentially useful for improving the video surveillance.