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Weakly supervised detection of video events using hidden conditional random fields

  • Kimiaki ShirahamaEmail author
  • Marcin Grzegorzek
  • Kuniaki Uehara
Regular Paper

Abstract

Multimedia Event Detection (MED) is the task to identify videos in which a certain event occurs. This paper addresses two problems in MED: weakly supervised setting and unclear event structure. The first indicates that since associations of shots with the event are laborious and incur annotator’s subjectivity, training videos are loosely annotated as to whether the event is contained or not. It is unknown which shots are relevant or irrelevant to the event. The second problem is the difficulty of assuming the event structure in advance, due to arbitrary camera and editing techniques. To tackle these problems, we propose a method using a Hidden Conditional Random Field (HCRF) which is a probabilistic discriminative classifier with a set of hidden states. We consider that the weakly supervised setting can be handled using hidden states as the intermediate layer to discriminate between relevant and irrelevant shots to the event. In addition, an unclear structure of the event can be exposed by features of each hidden state and its relation to the other states. Based on the above idea, we optimise hidden states and their relation so as to distinguish training videos containing the event from the others. Also, to exploit the full potential of HCRFs, we establish approaches for training video preparation, parameter initialisation and fusion of multiple HCRFs. Experimental results on TRECVID video data validate the effectiveness of our method.

Keywords

Multimedia event detection Hidden conditional random fields Weakly supervised setting Unclear event structure 

Notes

Acknowledgments

The research work by Kimiaki Shirahama leading to this article has been funded by the Postdoctoral Fellowship for Research Abroad by Japan Society for the Promotion of Science (JSPS). Also, this work was in part supported by JSPS through Grand-in-Aid for Scientific Research (B): KAKENHI (26280040).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Kimiaki Shirahama
    • 1
    Email author
  • Marcin Grzegorzek
    • 1
  • Kuniaki Uehara
    • 2
  1. 1.Pattern Recognition GroupUniversity of SiegenSiegenGermany
  2. 2.Graduate School of System InformaticsKobe UniversityKobeJapan

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