Graph based transductive learning for cartoon correspondence construction

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	Graph based transductive learning for cartoon correspondence construction
	Yu, Jun ; Bian, Wei ; Song, Mingli ; Cheng, Jun ; Tao, Dacheng ; Yu J(俞俊)
刊名	http://dx.doi.org/10.1016/j.neucom.2011.10.003
	2012-03-01
关键词	SHAPE CONTEXTS SUBSPACE RECOGNITION DECOMPOSITION SIMILARITY RETRIEVAL
英文摘要	National Natural Science Foundation of China [61100104, 61170142, 60806050, 60873124]; Key Laboratory of Robotics and Intelligent System of Guangdong Province [2009A060800016]; CAS [ZNGZ-2011-012]; Fundamental Research Funds for the Central Universities of the Republic of China [2010121066]; Shenzhen-Hong Kong Innovation Circle [JSE201007200037A]; Shenzhen Key Laboratory of Precision Engineering [CXB201005250018A]; Correspondence construction of characters in key frames is the prerequisite for cartoon animations' automatic inbetweening and coloring. Since each frame of an animation consists of multiple layers, characters are complicated in terms of shape and structure. Therefore, existing shape matching algorithms, specifically designed for simple structures such as a single closed contour, cannot perform well on characters constructed by multiple contours. This paper proposes an automatic cartoon correspondence construction approach with iterative graph based transductive learning (Graph-TL) and distance metric learning (DML) estimation. In details, this new method defines correspondence construction as a many-to-many labeling problem, which assigns the points from one key frame into the points from another key frame. Then, to refine the correspondence construction, we adopt an iterative optimization scheme to alternatively carry out the Graph-TL and DML estimation. In addition, in this paper, we adopt the local shape descriptor for cartoon application, which can successfully achieve rotation and scale invariance in cartoon matching. Plenty of experimental results on our cartoon dataset, which is built upon industrial production suggest the effectiveness of the proposed methods for constructing correspondences of complicated characters. (C) 2011 Elsevier B.V. All rights reserved.
语种	英语
出版者	NEUROCOMPUTING
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92365]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Yu, Jun,Bian, Wei,Song, Mingli,et al. Graph based transductive learning for cartoon correspondence construction[J]. http://dx.doi.org/10.1016/j.neucom.2011.10.003,2012.
APA	Yu, Jun,Bian, Wei,Song, Mingli,Cheng, Jun,Tao, Dacheng,&俞俊.(2012).Graph based transductive learning for cartoon correspondence construction.http://dx.doi.org/10.1016/j.neucom.2011.10.003.
MLA	Yu, Jun,et al."Graph based transductive learning for cartoon correspondence construction".http://dx.doi.org/10.1016/j.neucom.2011.10.003 (2012).