Hierarchical Latent Dirichlet Allocation models for realistic action recognition

It has always been very difficult to recognize realistic actions from unconstrained videos because there are tremendous variations from camera motion, background clutter, object appearance and so on. In this paper, a Single-Feature Hierarchical Latent Dirichlet Allocation model called SF-HLDA by extending Latent Dirichlet Allocation to the hierarchical one is first proposed for realistic action recognition. And then, by extending SF-HLDA, we present another model called Multi-Feature Hierarchical Latent Dirichlet Allocation model MF-HLDA which can effectively fuse several different features into one model for recognizing the realistic actions. Experiments demonstrate the effectiveness of our proposed models.