Transfer tagging from image to video

Yang, Y; Huang, Z; Shen, HT

Transfer tagging from image to video

Yang, Y

Huang, Z Shen, HT

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Publication Type:: Conference Proceeding
Citation:: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1137 - 1140
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1137 - 1140	en_US
dc.identifier.isbn	9781450306164	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120536
dc.description.abstract	Nowadays massive amount of web video datum has been emerging on the Internet. To achieve an effective and efficient video retrieval, it is critical to automatically assign semantic keywords to the videos via content analysis. However, most of the existing video tagging methods suffer from the problem of lacking sufficient tagged training videos due to high labor cost of manual tagging. Inspired by the observation that there are much more well-labeled data in other yet relevant types of media (e.g. images), in this paper we study how to build a "cross-media tunnel" to transfer external tag knowledge from image to video. Meanwhile, the intrinsic data structures of both image and video spaces are well explored for inferring tags. We propose a Cross-Media Tag Transfer (CMTT) paradigm which is able to: 1) transfer tag knowledge between image and video by minimizing their distribution difference; 2) infer tags by revealing the underlying manifold structures embedded within both image and video spaces. We also learn an explicit mapping function to handle unseen videos. Experimental results have been reported and analyzed to illustrate the superiority of our proposal. Copyright 2011 ACM.	en_US
dc.relation.ispartof	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops	en_US
dc.relation.isbasedon	10.1145/2072298.2071958	en_US
dc.title	Transfer tagging from image to video	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Nowadays massive amount of web video datum has been emerging on the Internet. To achieve an effective and efficient video retrieval, it is critical to automatically assign semantic keywords to the videos via content analysis. However, most of the existing video tagging methods suffer from the problem of lacking sufficient tagged training videos due to high labor cost of manual tagging. Inspired by the observation that there are much more well-labeled data in other yet relevant types of media (e.g. images), in this paper we study how to build a "cross-media tunnel" to transfer external tag knowledge from image to video. Meanwhile, the intrinsic data structures of both image and video spaces are well explored for inferring tags. We propose a Cross-Media Tag Transfer (CMTT) paradigm which is able to: 1) transfer tag knowledge between image and video by minimizing their distribution difference; 2) infer tags by revealing the underlying manifold structures embedded within both image and video spaces. We also learn an explicit mapping function to handle unseen videos. Experimental results have been reported and analyzed to illustrate the superiority of our proposal. Copyright 2011 ACM.

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http://hdl.handle.net/10453/120536