Recognizing complex events in real movies by combining audio and video features

This paper proposes a novel approach in taking audio feature into account for better event recognition performance in recognizing complex events in real movies, since audio can provide strong evidence to certain events. In our method, local-space time feature and audio feature are firstly extracted...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2014-08, Vol.137, p.89-95
Main Authors: Du, Ji-Xiang, Zhai, Chuan-Min, Guo, Yi-Lan, Tang, Yuan-Yan, Chun Lung, Philip Chen
Format: Article
Language:English
Subjects:
Audio feature
Density
Event recognition
Feature extraction
Feature recognition
Local space-time features
Motion pictures
Self-organization feature map
Shot
Support vector machines
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Summary:This paper proposes a novel approach in taking audio feature into account for better event recognition performance in recognizing complex events in real movies, since audio can provide strong evidence to certain events. In our method, local-space time feature and audio feature are firstly extracted from the video sequences and then an individual video sequence is represented as a SOFM density map; finally we integrate such density map with SVM for recognition events. To evaluate effectiveness of this method, this paper uses the public Hollywood dataset, in this dataset the shot sequences have been collected from 32 different Hollywood movies and it includes 8 event classes. The presented result justifies the proposed method explicitly, improve the average accuracy and average precision compared to other relative approaches.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.04.053