A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction
文献类型：期刊论文
| 作者 | Zhang, Zhao1; Liu, Cheng-Lin2 ; Zhao, Ming-Bo3
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| 刊名 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
 |
| 出版日期 | 2015-04-01 |
| 卷号 | 6期号:1 |
| 关键词 | Algorithms Design Experimentation Performance Sparse projection low-rank recovery similarity preservation salient stroke feature extraction handwriting representation and recognition |
| 英文摘要 | In this article, we consider the problem of simultaneous low-rank recovery and sparse projection. More specifically, a new Robust Principal Component Analysis (RPCA)-based framework called Sparse Projection and Low-Rank Recovery (SPLRR) is proposed for handwriting representation and salient stroke feature extraction. In addition to achieving a low-rank component encoding principal features and identify errors or missing values from a given data matrix as RPCA, SPLRR also learns a similarity-preserving sparse projection for extracting salient stroke features and embedding new inputs for classification. These properties make SPLRR applicable for handwriting recognition and stroke correction and enable online computation. A cosine-similarity-style regularization term is incorporated into the SPLRR formulation for encoding the similarities of local handwriting features. The sparse projection and low-rank recovery are calculated from a convex minimization problem that can be efficiently solved in polynomial time. Besides, the supervised extension of SPLRR is also elaborated. The effectiveness of our SPLRR is examined by extensive handwritten digital repairing, stroke correction, and recognition based on benchmark problems. Compared with other related techniques, SPLRR delivers strong generalization capability and state-of-the-art performance for handwriting representation and recognition. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | FACE RECOGNITION ; PRESERVING PROJECTIONS ; CLASSIFICATION ; ALGORITHM |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000353638800009 |
| 公开日期 | 2015-09-22 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8116]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 作者单位 | 1.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zhao,Liu, Cheng-Lin,Zhao, Ming-Bo. A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2015,6(1). |
| APA | Zhang, Zhao,Liu, Cheng-Lin,&Zhao, Ming-Bo.(2015).A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,6(1). |
| MLA | Zhang, Zhao,et al."A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 6.1(2015). |
入库方式： OAI收割
来源：自动化研究所
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