Multimedia event detection using GMM supervectors and SVMS

In multimedia event detection, complex target events are extracted from a large set of consumer-generated videos taken in unconstrained environments. We devised a multimedia event detection method based on GMM supervectors and support vector machines (SVMs) using multiple features. A GMM supervector...

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Bibliographic Details
Published in2012 19th IEEE International Conference on Image Processing pp. 3089 - 3092
Main Authors Kamishima, Y., Inoue, N., Shinoda, K., Sato, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Event detection
Feature extraction
GMM-Supervector
Multimedia communication
Multimedia event detection
Streaming media
Support vector machines
Vectors
Videos
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Summary:In multimedia event detection, complex target events are extracted from a large set of consumer-generated videos taken in unconstrained environments. We devised a multimedia event detection method based on GMM supervectors and support vector machines (SVMs) using multiple features. A GMM supervector consists of the parameters of a Gaussian mixture model (GMM) for the distribution of local features extracted from a video clip. A GMM is regarded as an extension of the Bag-of-Words (BoW) to a probabilistic framework, and thus, it can be expected to be robust against the data insufficiency problem. This method outperformed previous methods including BoW in experiments using the dataset of the multimedia event detection task in TRECVID2010 and 2011.
ISBN:1467325341
9781467325349
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2012.6467553