Learning Temporal, Relational, Force-Dynamic Event Definitions from Video

We present and evaluate a novel implemented approach for learning to recognize events in video. First, we introduce a sublanguage of event logic, called k-AMA, that is sufficiently expressive to represent visual events yet sufficiently restrictive to support learning. Second, we develop a specificto-general learning algorithm for learning event definitions in k-AMA. Finally, we apply this algorithm to the task of learning event definitions from video and show that it yields definitions that are competitive with hand-coded ones.

Authors

• Alan Fern Oregon State University
• and Robert Givan Purdue University

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