Human action recognition using modified slow feature analysis and multiple kernel learning
A novel human action recognition method is proposed, which includes two periods of action feature extraction and action recognition. Firstly, we use a modified slow feature analysis (SFA) to extract video local feature. Unlike slow feature analysis, we redefine the objective function with supervised information, which make the modified SFA more suitable to preserve the slow feature and label information. Meanwhile, in effort to cope with the dimension explosion in SFA, locality preserving projections (LPP) is used to reduce the quadratic expansion dimension. Secondly, we use a multiple kernel learning method (MKL) to classify human action, in which the weights of different kernels are optimized by combining Bacterial Chemotaxis method and Powell method. The results of experiments indicate the efficiency of our method.