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タイトル
和文: 
英文:Event detection in consumer videos using GMM supervectors and SVMs 
著者
和文: 上嶋 勇祐, 井上 中順, 篠田 浩一.  
英文: Yusuke Kamishima, Nakamasa Inoue, Koichi Shinoda.  
言語 English 
掲載誌/書名
和文: 
英文:EURASIP Journal on Image and Video Processing 
巻, 号, ページ vol. 2013:51        pp. 1-13
出版年月 2013年9月2日 
出版者
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英文: 
会議名称
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開催地
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ファイル
公式リンク http://jivp.eurasipjournals.com/content/2013/1/51
 
DOI https://doi.org/10.1186/1687-5281-2013-51
アブストラクト In large-scale multimedia event detection, complex target events are extracted from a large set of consumer-generated web videos taken in unconstrained environments. We devised a multimedia event detection method based on Gaussian mixture model (GMM) supervectors and support vector machines. A GMM supervector consists of the parameters of a GMM for the distribution of low-level features extracted from a video clip. A GMM is regarded as an extension of the bag-of-words framework to a probabilistic framework, and thus, it can be expected to be robust against the data insufficiency problem. We also propose a camera motion cancelled feature, which is a spatio-temporal feature robust against camera motions found in consumer-generated web videos. By combining these methods with the existing features, we aim to construct a high-performance event detection system. The effectiveness of our method is evaluated using TRECVID MED task benchmark. Keywords: Multimedia event detection; Feature extraction; GMM supervector; Support vector machines; Camera motion cancelled features

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