中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Large Margin Dimensionality Reduction for Action Similarity Labeling.

文献类型:期刊论文

作者Xiaojiang Peng; Yu Qiao; Qiang Peng; Qionghua Wang
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2014
英文摘要Action recognition in videos is receiving extensive research interest due to its wide applications. This task needs to assign a specific action class for each video. In this paper, we study the problem of action similarity labeling (ASLAN) that is to verify whether two action videos present the same type of action or not. We show that both Fisher vector (FV) and vector of locally aggregated descriptors (VLAD) with dense trajectory features can achieve state-of-the-art performance on the ASLAN benchmark. Our main contribution is to develop a large margin dimensionality reduction (LMDR) method to compress high-dimensional FV and VLAD. Specially, we leverage the hinge loss objective function and stochastic gradient descent to optimize the discriminative projection matrix of these vectors. Extensive experiments on the ASLAN dataset indicate that our LMDR method not only reduces the dimension significantly but also improves the verification performance.
收录类别SCI
原文出处http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6807695
语种英语
源URL[http://ir.siat.ac.cn:8080/handle/172644/5336]  
专题深圳先进技术研究院_集成所
作者单位IEEE SIGNAL PROCESSING LETTERS
推荐引用方式
GB/T 7714
Xiaojiang Peng,Yu Qiao,Qiang Peng,et al. Large Margin Dimensionality Reduction for Action Similarity Labeling.[J]. IEEE SIGNAL PROCESSING LETTERS,2014.
APA Xiaojiang Peng,Yu Qiao,Qiang Peng,&Qionghua Wang.(2014).Large Margin Dimensionality Reduction for Action Similarity Labeling..IEEE SIGNAL PROCESSING LETTERS.
MLA Xiaojiang Peng,et al."Large Margin Dimensionality Reduction for Action Similarity Labeling.".IEEE SIGNAL PROCESSING LETTERS (2014).

入库方式: OAI收割

来源:深圳先进技术研究院

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