Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization

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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)
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2013
卷号	22 期号:8 页码:3179-3191
关键词	Low rank online learning metric learning semi-supervised learning scene categorization
ISSN号	1057-7149
通讯作者	丛杨
产权排序	1
中文摘要	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.
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]	CLASSIFICATION ; TRACKING
收录类别	SCI ; EI
资助信息	Natural Science Foundation of China [61105013]; Nanyang Assistant Professorship [M4080134]; NTU CoE Seed [M4081039]
语种	英语
WOS记录号	WOS:000321926600022
公开日期	2013-10-05
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/12565]
专题	沈阳自动化研究所_机器人学研究室
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.