Unordered rule discovery using Ant Colony Optimization

Human behavior recognition is one important task of image processing and surveillance system. One main challenge of human behavior recognition is how to effectively model behaviors on condition of unconstrained videos due to tremendous variations from camera motion, background clutter, object appearance and so on. In this paper, we propose two novel Multi-Feature Hierarchical Latent Dirichlet Allocation models for human behavior recognition by extending the bag-of-word topic models such as the Latent Dirichlet Allocation model and the Multi-Modal Latent Dirichlet Allocation model. The two proposed models with three hierarchies including low-level visual features, feature topics, and behavior topics can effectively fuse two different types of features including motion and static visual features, avoid detecting or tracking the motion objects, and improve the recognition performance even if the features are extracted with a great amount of noise. Finally, we adopt the variational EM algorithm to learn the parameters of these models. Experiments on the YouTube dataset demonstrate the effectiveness of our proposed models.