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Low-Rank Online Metric Learning
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
专著(文集)名Low-Rank and Sparse Modeling for Visual Analysis
编著者中国科学院沈阳自动化研究所 ; Fu, Yun
出版者Springer
出版地Berlin
2014
ISBN号978-3-319-11999-1
出处页码203-234
收录类别EI
EI收录号20172803914384
关键词Low-rank Online Learning Metric Learning Image Categorization
摘要Image classification is a key problem in computer vision community. Most of the conventional visual recognition systems usually train an image classifier in an offline batch mode with all training data provided in advance. Unfortunately in many practical applications, usually only a small amount of training samples are available in the initialization stage and many more would come sequentially during the online process. Because the image data characteristics could dramatically change over time, it is important for the classifier to adapt to the new data incrementally. In this chapter, we present an online metric learning model to address the online image classification/scene recognition problem via adaptive similarity measurement. Given a number of labeled samples 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 provides competitive performance compared with the state-of-the-art methods, but also guarantees to converge. A bi-linear graph is also applied 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 new samples that are more confident labeled. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-update. We also demonstrate that the low-rank property widely exists in natural data. In the experiments, 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
产权排序1
文献类型专著章节/文集论文
条目标识符http://ir.sia.cn/handle/173321/15490
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.Department of Computer Science, University of Rochester, Rochester, USA
3.Department of Computer Science, University of Rochester, Rochester 14627, USA
4.School of EEE, Nanyang Technological University, Singapore 639798, Singapore
推荐引用方式
GB/T 7714
Cong Y,Liu J,Yuan JS,et al. Low-Rank Online Metric Learning. Low-Rank and Sparse Modeling for Visual Analysis. Berlin:Springer,2014:203-234.
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