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Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map based Feature Extraction for Human Action Recognition
文献类型:会议论文
作者 | Du, Yang1,2,3; Yuan, Chunfeng1; Hu, Weiming1; Yang, Hao1,2,3 |
出版日期 | 2018 |
会议日期 | 20180202-20180207 |
会议地点 | New Orleans, Louisiana, USA |
关键词 | Action Recognition Feature Extraction |
英文摘要 | Feature extraction is a critical step in the task of action recognition. Hand-crafted features are often restricted because of their fixed forms and deep learning features are more effective but need large-scale labeled data for training. In this paper, we propose a new hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map (NOASSOM) to
adaptively and learn effective features from data without supervision. NOASSOM is extended from Adaptive-Subspace Self-Organizing Map (ASSOM) which only deals with linear data and is trained with supervision by the labeled data. Firstly, by adding a nonlinear orthogonal map layer, NOASSOM is able to handle the nonlinear input data and it avoids defining the specific form of the nonlinear orthogonal map by a kernel trick. Secondly, we modify loss function of ASSOM such that every input sample is used to train model individually. In this way, NOASSOM effectively learns the statistic patterns from data without supervision. Thirdly, we propose a hierarchical NOASSOM to extract more representative
features. Finally, we apply the proposed hierarchical NOASSOM
to efficiently describe the appearance and motion information
around trajectories for action recognition. Experimental
results on widely used datasets show that our method has
superior performance than many state-of-the-art hand-crafted
features and deep learning features based methods. |
会议录 | 2018 AAAI Conference on Artificial Intelligence |
源URL | [http://ir.ia.ac.cn/handle/173211/19734] |
专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
通讯作者 | Yuan, Chunfeng |
作者单位 | 1.CAS Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.MTdata, Meitu |
推荐引用方式 GB/T 7714 | Du, Yang,Yuan, Chunfeng,Hu, Weiming,et al. Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map based Feature Extraction for Human Action Recognition[C]. 见:. New Orleans, Louisiana, USA. 20180202-20180207. |
入库方式: OAI收割
来源:自动化研究所
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