GRAPH-BASED MULTIPLE INSTANCE LEARNING FOR ACTION RECOGNITION

This paper presents a novel framework for recognizing realistic actions captured from unconstrained environments. We describe an action as a collection of space-time activity parts, which are adaptively extracted by clustering foreground trajectories. Each video part is associated with a Bag-of-Features (BoF) histogram, yielding our bag-of-histograms representation for video. We formulate our action classification problem within the graph-based Multiple Instance Learning (MIL) framework, in which each activity part is cast as an instance and a graphical model is incorporated with MIL to leverage the interaction information among the instances. We evaluate our method on two challenging action datasets and demonstrate significant improvements over the state-of-the-art BoF baseline algorithm.