Cartoon features selection using Diffusion Score

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	Cartoon features selection using Diffusion Score
	Yu, Jun ; Yu J(俞俊)
刊名	http://dx.doi.org/10.1016/j.sigpro.2012.06.011
	2013
关键词	RECOGNITION CLASSIFICATION SIMILARITY SUBSPACE
英文摘要	National Natural Science Foundation of China [61100104]; Natural Science Foundation of Fujian Province of China [2012J01287]; National Defense Basic Scientific Research program of China [B1420110155]; Similarity estimation is critical for the computer-assisted cartoon animation system to improve the efficiency of cartoon generations. The main issue in similarity estimation is choosing efficient features to describe cartoon images. Previous methods adopt pairwise distance to evaluate the similarity. However, this measurement is sensitive to noise. This paper proposes a novel feature selection method named Diffusion Score which captures the geometrical properties of the data structure by preserving the diffusion distance. Specifically, the Markov process is carried out to find meaningful geometric descriptions of the whole cartoon dataset. The diffusion distance sums over all paths' lengths which connect two data points. Since diffusion distance integrates "volume" of paths connecting data points, it is tolerant to noises. The time scale of Markov process can incorporate the cluster structure of data at different levels of granularity. It makes the number of the nearest neighbor K in graph construction to be an insensitive parameter. Therefore, Laplacian Score is sensitive in feature selection. Diffusion Score can effectively improve the stability by minimizing large absolute errors and large relative errors of the features. The experimental results can demonstrate the efficient performance of Diffusion Score in feature selection. (C) 2012 Elsevier B.V. All rights reserved.
语种	英语
出版者	ELSEVIER SCIENCE BV
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92570]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Yu, Jun,Yu J. Cartoon features selection using Diffusion Score[J]. http://dx.doi.org/10.1016/j.sigpro.2012.06.011,2013.
APA	Yu, Jun,&俞俊.(2013).Cartoon features selection using Diffusion Score.http://dx.doi.org/10.1016/j.sigpro.2012.06.011.
MLA	Yu, Jun,et al."Cartoon features selection using Diffusion Score".http://dx.doi.org/10.1016/j.sigpro.2012.06.011 (2013).