Tag-based social image search with visual-text joint hypergraph learning

Gao, Y; Wang, M; Luan, H; Shen, J; Yan, S; Tao, D

Tag-based social image search with visual-text joint hypergraph learning

Gao, Y Wang, M Luan, H Shen, J Yan, S Tao, D

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Publication Type:: Conference Proceeding
Citation:: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1517 - 1520
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Luan, H	en_US
dc.contributor.author	Shen, J	en_US
dc.contributor.author	Yan, S	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	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. 1517 - 1520	en_US
dc.identifier.isbn	9781450306164	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31174
dc.description.abstract	Tag-based social image search has attracted great interest and how to order the search results based on relevance level is a research problem. Visual content of images and tags have both been investigated. However, existing methods usually employ tags and visual content separately or sequentially to learn the image relevance. This paper proposes a tag-based image search with visual-text joint hypergraph learning. We simultaneously investigate the bag-of-words and bag-of-visual-words representations of images and accomplish the relevance estimation with a hypergraph learning approach. Each textual or visual word generates a hyperedge in the constructed hypergraph. We conduct experiments with a real-world data set and experimental results demonstrate the effectiveness of our approach. 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	10.1145/2072298.2072054	en_US
dc.title	Tag-based social image search with visual-text joint hypergraph learning	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Scottsdale, Arizona, USA
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Tag-based social image search has attracted great interest and how to order the search results based on relevance level is a research problem. Visual content of images and tags have both been investigated. However, existing methods usually employ tags and visual content separately or sequentially to learn the image relevance. This paper proposes a tag-based image search with visual-text joint hypergraph learning. We simultaneously investigate the bag-of-words and bag-of-visual-words representations of images and accomplish the relevance estimation with a hypergraph learning approach. Each textual or visual word generates a hyperedge in the constructed hypergraph. We conduct experiments with a real-world data set and experimental results demonstrate the effectiveness of our approach. Copyright 2011 ACM.

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http://hdl.handle.net/10453/31174