Extracting non-negative basis images using pixel dispersion penalty
文献类型：期刊论文
| 作者 | Zheng, Wei-Shi1,4; Lai, JianHuang1; Liao, Shengcai2; He, Ran3; Ran He(赫然) ; Shengcai Liao
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2012-08-01 |
| 卷号 | 45期号:8页码:2912-2926 |
| 关键词 | Non-negative matrix factorization (NMF) Non-negativity constraint Spatially localized basis images Feature extraction Face image analysis |
| 英文摘要 | Non-negativity matrix factorization (NMF) and its variants have been explored in the last decade and are still attractive due to its ability of extracting non-negative basis images. However, most existing NMF based methods are not ready for encoding higher-order data information. One reason is that they do not directly/explicitly model structured data information during learning, and therefore the extracted basis images may not completely describe the "parts" in an image [1] very well. In order to solve this problem, the structured sparse NMF has been recently proposed in order to learn structured basis images. It however depends on some special prior knowledge, i.e. one needs to exhaustively define a set of structured patterns in advance. In this paper, we wish to perform structured sparsity learning as automatically as possible. To that end, we propose a pixel dispersion penalty (PDP), which effectively describes the spatial dispersion of pixels in an image without using any manually predefined structured patterns as constraints. In PDP, we consider each part-based feature pattern of an image as a cluster of non-zero pixels; that is the non-zero pixels of a local pattern should be spatially close to each other. Furthermore, by incorporating the proposed PDP, we develop a spatial non-negative matrix factorization (Spatial NMF) and a spatial non-negative component analysis (Spatial NCA). In Spatial NCA, the non-negativity constraint is only imposed on basis images and such constraint on coefficients is released, so both subtractive and additive combinations of non-negative basis images are allowed for reconstructing any images. Extensive experiments are conducted to validate the effectiveness of the proposed pixel dispersion penalty. We also experimentally show that Spatial NCA is more flexible for extracting non-negative basis images and obtains better and more stable performance. (C) 2012 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | MATRIX FACTORIZATION ; REGRESSION ; PARTS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000303294500005 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3821]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 中国科学院自动化研究所 |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Informat Sci & Technol, Dept Elect & Commun Engn, Guangzhou 510275, Guangdong, Peoples R China 2.Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 4.Sun Yat Sen Univ, People Program 100, Guangzhou 510275, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Wei-Shi,Lai, JianHuang,Liao, Shengcai,et al. Extracting non-negative basis images using pixel dispersion penalty[J]. PATTERN RECOGNITION,2012,45(8):2912-2926. |
| APA | Zheng, Wei-Shi,Lai, JianHuang,Liao, Shengcai,He, Ran,Ran He,&Shengcai Liao.(2012).Extracting non-negative basis images using pixel dispersion penalty.PATTERN RECOGNITION,45(8),2912-2926. |
| MLA | Zheng, Wei-Shi,et al."Extracting non-negative basis images using pixel dispersion penalty".PATTERN RECOGNITION 45.8(2012):2912-2926. |
入库方式： OAI收割
来源：自动化研究所
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