Yusuke Kamishima

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The aim of this section is to develop a high-performance semantic indexing system using Gaussian mixture model (GMM) supervectors and tree-structured GMMs [1, 2]. GMM spervectors corresponding to six types of audio and visual features are extracted from video shots by using tree-structured GMMs. The computational cost of maximum a posteriori (MAP)(More)
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(More)
We combine local features and global features and use them for detecting events. Local features are based on a person detector and they represent movements of individuals. Global features are based on optical flow features in video frames and they represent the flow of people. We tried to detect PersonRuns, PeopleMeet and PeopleSplitUp events. The results(More)
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(More)
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