Machine Vision and Applications

, Volume 25, Issue 1, pp 49–69 | Cite as

Multimedia event detection with multimodal feature fusion and temporal concept localization

  • Sangmin Oh
  • Scott McCloskey
  • Ilseo Kim
  • Arash Vahdat
  • Kevin J. Cannons
  • Hossein Hajimirsadeghi
  • Greg Mori
  • A. G. Amitha Perera
  • Megha Pandey
  • Jason J. Corso
Special Issue Paper

Abstract

We present a system for multimedia event detection. The developed system characterizes complex multimedia events based on a large array of multimodal features, and classifies unseen videos by effectively fusing diverse responses. We present three major technical innovations. First, we explore novel visual and audio features across multiple semantic granularities, including building, often in an unsupervised manner, mid-level and high-level features upon low-level features to enable semantic understanding. Second, we show a novel Latent SVM model which learns and localizes discriminative high-level concepts in cluttered video sequences. In addition to improving detection accuracy beyond existing approaches, it enables a unique summary for every retrieval by its use of high-level concepts and temporal evidence localization. The resulting summary provides some transparency into why the system classified the video as it did. Finally, we present novel fusion learning algorithms and our methodology to improve fusion learning under limited training data condition. Thorough evaluation on a large TRECVID MED 2011 dataset showcases the benefits of the presented system.

Keywords

Multimedia Classification Machine learning Fusion 

Notes

Acknowledgments

This work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20069. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/NBC, or the U.S. Government.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sangmin Oh
    • 1
  • Scott McCloskey
    • 2
  • Ilseo Kim
    • 1
  • Arash Vahdat
    • 3
  • Kevin J. Cannons
    • 3
  • Hossein Hajimirsadeghi
    • 3
  • Greg Mori
    • 3
  • A. G. Amitha Perera
    • 1
  • Megha Pandey
    • 1
  • Jason J. Corso
    • 4
  1. 1.Kitware Inc.Clifton ParkUSA
  2. 2.Honeywell LabsMinneapolisUSA
  3. 3.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  4. 4.Department of Computer Science and EngineeringSUNY at BuffaloBuffaloUSA

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