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Title
Japanese: 
English:Event detection in consumer videos using GMM supervectors and SVMs 
Author
Japanese: 上嶋 勇祐, 井上 中順, 篠田 浩一.  
English: Yusuke Kamishima, Nakamasa Inoue, Koichi Shinoda.  
Language English 
Journal/Book name
Japanese: 
English:EURASIP Journal on Image and Video Processing 
Volume, Number, Page vol. 2013:51        pp. 1-13
Published date Sep 2, 2013 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
File
Official URL http://jivp.eurasipjournals.com/content/2013/1/51
 
Abstract 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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