ISCAS OpenIR  > 2010软件所会议论文
face sketch synthesis via sparse representation
Chang Liang; Zhou Mingquan; Han Yanjun; Deng Xiaoming
2010
会议名称2010 20th International Conference on Pattern Recognition, ICPR 2010
会议录名称Proceedings - International Conference on Pattern Recognition
页码2146-2149
会议日期August 23,
会议地点Istanbul, Turkey
收录类别ei
出版地United States
ISSN10514651
ISBN9780770000000
部门归属(1) College of Information Science and Technology, Beijing Normal University, Beijing 100875, China; (2) Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (3) Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
摘要Face sketch synthesis with a photo is challenging due to that the psychological mechanism of sketch generation is difficult to be expressed precisely by rules. Current learning-based sketch synthesis methods concentrate on learning the rules by optimizing cost functions with low-level image features. In this paper, a new face sketch synthesis method is presented, which is inspired by recent advances in sparse signal representation and neuroscience that human brain probably perceives images using high-level features which are sparse. Sparse representations are desired in sketch synthesis due to that sparseness can adaptively selects the most relevant samples which give best representations of the input photo. We assume that the face photo patch and its corresponding sketch patch follow the same sparse representation. In the feature extraction, we select succinct high-level features by using the sparse coding technique, and in the sketch synthesis process each sketch patch is synthesized with respect to high-level features by solving an l1-norm optimization. Experiments have been given on CUHK database to show that our method can resemble the true sketch fairly well. © 2010 IEEE.
关键词Cost Functions Image Processing Optimization
语种英语
内容类型会议论文
URI标识http://ir.iscas.ac.cn/handle/311060/8758
专题2010软件所会议论文
推荐引用方式
GB/T 7714
Chang Liang,Zhou Mingquan,Han Yanjun,et al. face sketch synthesis via sparse representation[C]. United States,2010:2146-2149.
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