Transductive cartoon retrieval by multiple hypergraph learning

Yu, J; Cheng, J; Wang, J; Tao, D

Transductive cartoon retrieval by multiple hypergraph learning

Yu, J Cheng, J Wang, J Tao, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7665 LNCS (PART 3), pp. 269 - 276
Issue Date:: 2012-11-19

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Field	Value	Language
dc.contributor.author	Yu, J	en_US
dc.contributor.author	Cheng, J	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-11-19	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, 7665 LNCS (PART 3), pp. 269 - 276	en_US
dc.identifier.isbn	9783642344862	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22905
dc.description.abstract	Cartoon characters retrieval frequently suffers from the distance estimation problem. In this paper, a multiple hypergraph fusion based approach is presented to solve this problem. We build multiple hypergraphs on cartoon characters based on their features. In these hypergraphs, each vertex is a character, and an edge links to multiple vertices. In this way, the distance estimation between characters is avoided and the high-order relationship among characters can be explored. The experiments of retrieval are conducted on cartoon datasets, and the results demonstrate that the proposed approach can achieve better performance than state-of-the-arts methods. © 2012 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-34487-9_33	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Transductive cartoon retrieval by multiple hypergraph learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	7665 LNCS	en_US
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
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7665 LNCS	en_US

Abstract:

Cartoon characters retrieval frequently suffers from the distance estimation problem. In this paper, a multiple hypergraph fusion based approach is presented to solve this problem. We build multiple hypergraphs on cartoon characters based on their features. In these hypergraphs, each vertex is a character, and an edge links to multiple vertices. In this way, the distance estimation between characters is avoided and the high-order relationship among characters can be explored. The experiments of retrieval are conducted on cartoon datasets, and the results demonstrate that the proposed approach can achieve better performance than state-of-the-arts methods. © 2012 Springer-Verlag.

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http://hdl.handle.net/10453/22905