*5 ATR Intelligent Robotics and Communication Laboratories

This paper proposes a novel mining method for multimodal interactions to extract important patterns of group activities. These extracted patterns can be used as machinereadable event indices in developing an interaction corpus based on a huge collection of human interaction data captured by various sensors. The event indices can be used, for example, to summarize a set of events and to search for particular events because they contain various pieces of context information. The proposed method extracts simultaneously occurring patterns of primitive events in interaction, such as gaze and speech, that in combination occur more consistently than randomly. The proposed method provides a statistically plausible definition of interaction events that is not possible through intuitive top-down definitions. We demonstrate the effectiveness of our method for the data captured in an experimental setup of a poster-exhibition scene. Several interesting patterns are extracted by the method, and we examined their interpretations