Group sparse based locality - sensitive dictionary learning for video semantic analysis

摘要

Sparse Representation-based Classifier (SRC) and Dictionary Learning (DL), have significantly impacted greatly on the classification performance of image recognition in recent times. In video semantic analysis, the locality structure of video semantic data containing more discriminative information is very essential for classification. However, this has not been fully considered by the current sparse representation-based approaches. Furthermore, similar coding outcomes are not being realized from video features with the same video category. To handle these issues, we propose a novel DL method, called Group Sparsity Locality-Sensitive Dictionary Learning (GSLSDL) for video semantic analysis. In the proposed GSLSDL, a discriminant loss function for the video category based on group sparse coding of sparse coefficients, is introduced into the structure of the Locality-Sensitive Dictionary Learning (LSDL) method. After solving the optimized dictionary, the sparse coefficients for the testing video feature samples are obtained. The classification result for video semantic is then realized by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed GSLSDL significantly improves the performance of video semantic detection compared with the competing methods, and robust in various diverse environments of video.