Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization
文献类型：期刊论文
| 作者 | Feng, Songhe1,2; Xiong, Weihua3; Li, Bing3 ; Lang, Congyan1; Huang, Xiankai4
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| 刊名 | SIGNAL PROCESSING
 |
| 出版日期 | 2014 |
| 卷号 | 94页码:595-607 |
| 关键词 | Multi-Instance Semi-Supervised Learning Hierarchical sparse representation Weighted multi-instance kernel Image categorization |
| 英文摘要 | Recent studies have shown that sparse representation (SR) can deal well with many computer vision problems. In this paper, we extend a hierarchical sparse representation algorithm into Multi-Instance Semi-Supervised Learning (MISSL) problem. Specifically, at the instance level, after investigating the properties of true positive instances in depth, we propose a novel instance disambiguation strategy based on sparse representation that can identify the instance confidence value in both positive and unlabeled bags more effectively. At the bag level, in contrast to the traditional k-NN or epsilon-graph construction methods used in the graph-based semi-supervised learning settings, we propose a weighted multi-instance kernel and a corresponding kernel sparse representation method for robust l(1)-graph construction. The improved e(1)-graph that encodes the multi-instance properties can be utilized in the manifold regularization framework for the label propagation. Experimental results on different image data sets have demonstrated that the proposed algorithm outperforms existing multi-instance learning (MIL) algorithms, as well as the MISSL algorithms with the application to image categorization task. (C) 2013 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | FACE RECOGNITION ; REGULARIZATION ; CLASSIFICATION ; ANNOTATION ; FRAMEWORK ; RETRIEVAL |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000327363300062 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3283]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 作者单位 | 1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China 2.Beijing Jiaotong Univ, Beijing Key Lab Transportat Data Anal & Min, Beijing, Peoples R China 3.Chinese Acad Sci, NLPR, Inst Automat, Beijing, Peoples R China 4.Beijing Union Univ, Tourism Inst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Songhe,Xiong, Weihua,Li, Bing,et al. Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization[J]. SIGNAL PROCESSING,2014,94:595-607. |
| APA | Feng, Songhe,Xiong, Weihua,Li, Bing,Lang, Congyan,&Huang, Xiankai.(2014).Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization.SIGNAL PROCESSING,94,595-607. |
| MLA | Feng, Songhe,et al."Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization".SIGNAL PROCESSING 94(2014):595-607. |
入库方式： OAI收割
来源：自动化研究所
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