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Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization
Cong Y(丛杨); Liu J(刘霁); Yuan JS(袁浚菘); Luo JB(罗杰波)
作者部门机器人学研究室
关键词Low Rank Online Learning Metric Learning Semi-supervised Learning Scene Categorization
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2013
卷号22期号:8页码:3179-3191
收录类别SCI ; EI
EI收录号20132516440364
WOS记录号WOS:000321926600022
产权排序1
资助机构Natural Science Foundation of China [61105013]; Nanyang Assistant Professorship [M4080134]; NTU CoE Seed [M4081039]
摘要Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]CLASSIFICATION ; TRACKING
WOS研究方向Computer Science ; Engineering
引用统计
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/12565
专题机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.School of Electrical and Electronics Engineering, Nanyang Technological University, 639798 Singapore, Singapore
3.Department of Computer Science, University of Wisconsin, Wisconsin, MA 53706, United States
4.Department of Computer Science, University of Rochester, Rochester, NY 14627, United States
推荐引用方式
GB/T 7714
Cong Y,Liu J,Yuan JS,et al. Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(8):3179-3191.
APA Cong Y,Liu J,Yuan JS,&Luo JB.(2013).Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(8),3179-3191.
MLA Cong Y,et al."Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.8(2013):3179-3191.
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