Weakly Supervised Learning of Part-based Models for Interaction Prediction via LDA
Jialin Chen · 2015
In this paper, we focus on interaction prediction which infers to what interaction might happen in the near future. Each interaction is modeled by mixtures of deformable parts in order to provide higher tolerance to part configurations. In our weakly supervised learning setting, part detectors are learned from training data without bounding boxes around the true locations of the people in each frame. The discriminating features are obtained using a two-layer Linear Discriminant Analysis (LDA) classification to promise maximal separability for parts and interactions respectively. Experimental results demonstrate that the proposed system is effective in learning part-based models in less annotated information and achieves comparable performance to state-of-the-art fully supervised approaches.