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Detecting complex events in user-generated video using concept classifiers

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4 Author(s)
Jinlin Guo ; CLARITY and School of Computing, Dublin City University, Glasnevin, Dublin 9, Dublin, Ireland ; David Scott ; Frank Hopfgartner ; Cathal Gurrin

Automatic detection of complex events in user-generated videos (UGV) is a challenging task due to its new characteristics differing from broadcast video. In this work, we firstly summarize the new characteristics of UGV, and then explore how to utilize concept classifiers to recognize complex events in UGV content. The method starts from manually selecting a variety of relevant concepts, followed by constructing classifiers for these concepts. Finally, complex event detectors are learned by using the concatenated probabilistic scores of these concept classifiers as features. Further, we also compare three different fusion operations of probabilistic scores, namely Maximum, Average and Minimum fusion. Experimental results suggest that our method provides promising results. It also shows that Maximum fusion tends to give better performance for most complex events.

Published in:

Content-Based Multimedia Indexing (CBMI), 2012 10th International Workshop on

Date of Conference:

27-29 June 2012