Ontology and Taxonomy Collaborated Framework for Meeting Classification

A framework for classification of meeting videos is proposed in this paper. Our goal is to utilize this framework to analyze human motion data to perform automatic meeting classification. We use a rule-based system and state machine to analyze the videos, utilize three levels of context hierarchy, namely movements (and their attributes), events(actions), and behavior to identify the activities and classify the meeting type based on the meeting ontology. We also define a meeting ontology that is determined by the knowledge base of various meeting sequences. This ontology validates and refines the taxonomy based on the hierarchy of events and behaviors, and regroups similar meetings in one category, refining the classes. Ontology is the process of determining the class of a meeting video based on relationships, and taxonomy is the categorization of meetings based on a certain criteria. The rule-based system is the primary framework manager, which recognizes behaviors based on the events detected by the state machine. It also periodically rolls back the state machine from erroneous statespace to a stable state. The state machine detects the events using a sliding temporal window of human movements. Our approach is appropriate for classifying meetings in complex sequences involving various actions and partial occlusion of tracked objects. Our framework is unique and scalable, with the capability to add new meeting types to the framework with little or no modification to the current framework. Using our framework, we are able to correctly classify various meeting sequences such as voting, argument, presentation, and object passing in our experiments. This framework is applicable to automated video surveillance, video segmentation and retrieval (multimedia), human computer in..