Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion For Action Recognition

Human action recognition is an area with increasing significance and has attracted much research attention over these years. Fusing multiple features is intuitively an appropriate way to better recognize actions in videos, as single type of features is not able to capture the visual characteristics sufficiently. However, most of the existing fusion methods used for action recognition fail to measure the contributions of different features and may not guarantee the performance improvement over the individual features. In this paper, we propose a new Hierarchical Bayesian Multiple Kernel Learning (HB-MKL) model to effectively fuse diverse types of features for action recognition. The model is able to adaptively evaluate the optimal weights of the base kernels constructed from different features to form a composite kernel. We evaluate the effectiveness of our method with the complementary features capturing both appearance and motion information from the videos on challenging human action datasets, and the experimental results demonstrate the potential of HB-MKL for action recognition.