Special Issue Paper

Machine Vision and Applications

, Volume 25, Issue 1, pp 17-32

Open Access This content is freely available online to anyone, anywhere at any time.

Evaluating multimedia features and fusion for example-based event detection

  • Gregory K. MyersAffiliated withSRI International (SRI) Email author 
  • , Ramesh NallapatiAffiliated withSRI International (SRI)IBM Thomas J Watson Research Center
  • , Julien van HoutAffiliated withSRI International (SRI)
  • , Stephanie PancoastAffiliated withSRI International (SRI)
  • , Ramakant NevatiaAffiliated withInstitute for Robotics and Intelligent Systems, University of Southern California (USC)
  • , Chen SunAffiliated withInstitute for Robotics and Intelligent Systems, University of Southern California (USC)
  • , Amirhossein HabibianAffiliated withUniversity of Amsterdam (UvA)
  • , Dennis C. KoelmaAffiliated withUniversity of Amsterdam (UvA)
  • , Koen E. A. van de SandeAffiliated withUniversity of Amsterdam (UvA)
    • , Arnold W. M. SmeuldersAffiliated withUniversity of Amsterdam (UvA)
    • , Cees G. M. SnoekAffiliated withUniversity of Amsterdam (UvA)

Abstract

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; high-level 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 performance in the 2012 TRECVID MED evaluation was one of the best reported.

Keywords

Multimedia event detection Video retrieval Content extraction Difference coding Late fusion