Recognizing Cartoon Image Gestures for Retrieval and Interactive Cartoon Clip Synthesis

Yang, Y; Zhuang, Y; Tao, D; Xu, D; Yu, J; Luo, J

Recognizing Cartoon Image Gestures for Retrieval and Interactive Cartoon Clip Synthesis

Yang, Y

Zhuang, Y Tao, D

Xu, D

Yu, J Luo, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20 (12), pp. 1745 - 1756
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Zhuang, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Yu, J	en_US
dc.contributor.author	Luo, J	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20 (12), pp. 1745 - 1756	en_US
dc.identifier.issn	1051-8215	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15246
dc.description.abstract	In this paper, we propose a new method to recognize gestures of cartoon images with two practical applications, i.e., content-based cartoon image retrieval and interactive cartoon clip synthesis. Upon analyzing the unique properties of four types of features including global color histogram, local color histogram (LCH), edge feature (EF), and motion direction feature (MDF), we propose to employ different features for different purposes and in various phases. We use EF to define a graph and then refine its local structure by LCH. Based on this graph, we adopt a transductive learning algorithm to construct local patches for each cartoon image. A spectral method is then proposed to optimize the local structure of each patch and then align these patches globally. MDF is fused with EF and LCH and a cartoon gesture space is constructed for cartoon image gesture recognition. We apply the proposed method to content-based cartoon image retrieval and interactive cartoon clip synthesis. The experiments demonstrate the effectiveness of our method. © 2010, IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology	en_US
dc.relation.isbasedon	10.1109/TCSVT.2010.2087452	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Recognizing Cartoon Image Gestures for Retrieval and Interactive Cartoon Clip Synthesis	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	20	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

In this paper, we propose a new method to recognize gestures of cartoon images with two practical applications, i.e., content-based cartoon image retrieval and interactive cartoon clip synthesis. Upon analyzing the unique properties of four types of features including global color histogram, local color histogram (LCH), edge feature (EF), and motion direction feature (MDF), we propose to employ different features for different purposes and in various phases. We use EF to define a graph and then refine its local structure by LCH. Based on this graph, we adopt a transductive learning algorithm to construct local patches for each cartoon image. A spectral method is then proposed to optimize the local structure of each patch and then align these patches globally. MDF is fused with EF and LCH and a cartoon gesture space is constructed for cartoon image gesture recognition. We apply the proposed method to content-based cartoon image retrieval and interactive cartoon clip synthesis. The experiments demonstrate the effectiveness of our method. © 2010, IEEE.

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http://hdl.handle.net/10453/15246