Chatting Activity Recognition Using Factorial Conditional Random Fields with Iterative Classification
Date Issued
2008
Date
2008
Author(s)
Lian, Chia-Chun
Abstract
Recognition of chatting activities occurring in social occasions is an important building block for constructing a 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, thereby causing a great deal of confusion for the recognition of multiple chatting activities. To model the conversational dynamics of co-existing chatting behaviors, I advocate using the Factorial Conditional Random Fields (FCRFs) to accommodate co-temporal relationships among multi-activity states and to reduce model complexity. In addition, to avoid the use of the inefficient Loopy Belief Propagation (LBP) algorithm, I propose using the Iterative Classification Algorithm (ICA) as the inference method for FCRFs. We designed several experiments to compare our FCRFs model with other dynamic probabilistic models, such as the Parallel Condition Random Fields (PCRFs) and the Hidden Markov Models (HMMs), in learning and decoding based on auditory data. While considering the existence of multiple concurrent chatting activities, the experimental results show that the FCRFs models outperform the PCRFs model and other HMMs-like models. We also discover that the FCRFs model using the ICA inference approach not only improves the recognition accuracy but also takes significantly much less time to do learning and decoding processes than the LBP inference method.
Subjects
Chatting Activity Recognition
Dynamic Probabilistic Models
Factorial Conditional Random Fields
Loopy Belief Propagation Algorithm
Iterative Classification Algorithm
Type
thesis
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