Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; highlevel semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s G. K. Myers (B) · R. Nallapati · J. van Hout · S. Pancoast SRI International (SRI), 333 Ravenswood Avenue, Menlo Park, CA 94025, USA e-mail: gregory.myers@sri.com R. Nevatia · C. Sun Institute for Robotics and Intelligent Systems, University of Southern California (USC), Los Angeles, CA 90089-0273, USA A. Habibian · D. C. Koelma · K. E. A. van de Sande · A. W. M. Smeulders · C. G. M. Snoek University of Amsterdam (UvA), Science Park 904, P.O. Box 94323, Amsterdam 1098 GH, The Netherlands R. Nallapati IBM Thomas J Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA performance in the 2012 TRECVID MED evaluation was one of the best reported.