Chatting Activity Recognition in Social Occasions Using Factorial Conditional Random Fields with Iterative Classification

Chia-chun Lian, Jane Yung-jen Hsu

Recognizing chatting activities occurring in social occasions plays an important role of building human social network. Among the various types of social interactions, chatting with others is a significant indicator. However, the main challenge of chatting activity recognition in public occasions is the existence of multiple people involved in multiple activities. That is, several conversations may take place concurrently, such that different combinations of multi-activity states will impact the final observations, causing a lot of confusion for the recognition of multiple chatting activities. To address this problem, we advocate using Factorial Conditional Random Fields (FCRFs) model to accommodate co-temporal relationships between multi-activity states. In addition, to avoid the use of inefficient Loopy Belief Propagation (LBP) algorithm, we propose using Iterative Classification Algorithm (ICA) as inference method to help accelerate learning and inferring process. We designed experiments to compare our FCRFs model with Linear Chain Condition Random Fields (LCRFs) in learning and performing inference with the 3-hour audio streams data set collected by 4 participants. The experimental results show that FCRFs models outperform the LCRFs model in the presence of multiple concurrent chatting activities, and the FCRFs model using ICA inference approach takes much less time to do learning process than LBP method.

Subjects: 12. Machine Learning and Discovery; 3.4 Probabilistic Reasoning

Submitted: Apr 8, 2008

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