Presentation	2011-06-07 Fast Semantic Indexing Using Tree-structured GMMs Nakamasa INOUE, Koichi SHINODA,
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Abstract(in English)	We propose a fast semantic indexing method for large scale video resources using tree-structured Gaussian mixture models (GMMs). GMM supervectors or Fisher vectors, which are used in state-of-the-art methods of semantic indexing, are rapidly extracted from a video shot by using the proposing method. Experiments on the TRECVID 2010 dataset demonstrate the effectiveness of our method. The calculation speed for estimating a GMM was 4.2 times faster than a conventional method on average.
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Keyword(in English)	Semantic Indexing / Tree-structured GMM / GMM Supervector
Paper #	DE2011-19,PRMU2011-50
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Committee	PRMU
Conference Date	2011/5/30(1days)
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Paper Information
Registration To	Pattern Recognition and Media Understanding (PRMU)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Fast Semantic Indexing Using Tree-structured GMMs
Sub Title (in English)
Keyword(1)	Semantic Indexing
Keyword(2)	Tree-structured GMM
Keyword(3)	GMM Supervector
1st Author's Name	Nakamasa INOUE
1st Author's Affiliation	Department of Computer Science, Tokyo Institute of Technology()
2nd Author's Name	Koichi SHINODA
2nd Author's Affiliation	Department of Computer Science, Tokyo Institute of Technology
Date	2011-06-07
Paper #	DE2011-19,PRMU2011-50
Volume (vol)	vol.111
Number (no)	77
Page	pp.pp.-
#Pages	6
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