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题名: Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization
作者: 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
刊名: IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN号: 1057-7149
出版日期: 2013
卷号: 22, 期号:8, 页码:3179-3191
收录类别: SCI ; EI
产权排序: 1
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记录号: WOS:000321926600022
WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
关键词[WOS]: CLASSIFICATION ; TRACKING
研究领域[WOS]: Computer Science ; Engineering
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/12565
Appears in Collections:机器人学研究室_期刊论文

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Recommended Citation:
丛杨; 刘霁; 袁浚菘; 罗杰波.Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization,IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(8):3179-3191
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文件名: Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization.pdf
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