Interactive cartoon reusing by transfer learning

Yu, J; Cheng, J; Tao, D

Interactive cartoon reusing by transfer learning

Yu, J Cheng, J Tao, D

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Publication Type:: Journal Article
Citation:: Signal Processing, 2012, 92 (9), pp. 2147 - 2158
Issue Date:: 2012-09-01

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Field	Value	Language
dc.contributor.author	Yu, J	en_US
dc.contributor.author	Cheng, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2012-09-01	en_US
dc.identifier.citation	Signal Processing, 2012, 92 (9), pp. 2147 - 2158	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22894
dc.description.abstract	Cartoon character retrieval is critical for cartoonists to effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features to comprehensively represent cartoon characters and accurately estimate dissimilarity between cartoon characters. In this paper, we define three visual features: Hausdorff contour feature (HCF), color histogram (CH) and motion feature (MF), to characterize the shape, color and motion structure information of a cartoon character. The HCF can be referred as intra-features, and the features of CH and MF can be regarded as inter-feature. However, due to the semantic gap, the cartoon retrieval by using these visual features still cannot achieve excellent performance. Since the labeling information has been proven effective to reduce the semantic gap, we introduce a labeling procedure called interactive cartoon labeling (ICL). The labeling information actually reflects user's retrieval purpose. A new dimension reduction tool, termed sparse transfer learning (SPA-TL), is adopted to effectively and efficiently encode user's search intention. In particular, SPA-TL exploits two pieces of knowledge data, i.e., the labeling knowledge contained in labeled data and the data distribution knowledge contained in all samples (labeled and unlabeled). The low-dimensional subspace is obtained by transferring the user feedback knowledge from labeled samples to unlabeled samples by preserving the sample distribution knowledge. Experimental evaluations in cartoon synthesis suggest the effectiveness of the visual features and SPA-TL. © 2012 Elsevier B.V. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120103730
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2012.01.028	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Interactive cartoon reusing by transfer learning	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	92	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	92	en_US

Abstract:

Cartoon character retrieval is critical for cartoonists to effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features to comprehensively represent cartoon characters and accurately estimate dissimilarity between cartoon characters. In this paper, we define three visual features: Hausdorff contour feature (HCF), color histogram (CH) and motion feature (MF), to characterize the shape, color and motion structure information of a cartoon character. The HCF can be referred as intra-features, and the features of CH and MF can be regarded as inter-feature. However, due to the semantic gap, the cartoon retrieval by using these visual features still cannot achieve excellent performance. Since the labeling information has been proven effective to reduce the semantic gap, we introduce a labeling procedure called interactive cartoon labeling (ICL). The labeling information actually reflects user's retrieval purpose. A new dimension reduction tool, termed sparse transfer learning (SPA-TL), is adopted to effectively and efficiently encode user's search intention. In particular, SPA-TL exploits two pieces of knowledge data, i.e., the labeling knowledge contained in labeled data and the data distribution knowledge contained in all samples (labeled and unlabeled). The low-dimensional subspace is obtained by transferring the user feedback knowledge from labeled samples to unlabeled samples by preserving the sample distribution knowledge. Experimental evaluations in cartoon synthesis suggest the effectiveness of the visual features and SPA-TL. © 2012 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/22894