Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition

Authors

  • Yang Du Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; MTdata, Meitu
  • Chunfeng Yuan Institute of Automation, Chinese Academy of Sciences
  • Bing Li Institute of Automation, Chinese Academy of Sciences
  • Weiming Hu Institute of Automation, Chinese Academy of Sciences
  • Hao Yang Institute of Automation, Chinese Academy of Sciencess; University of Chinese Academy of Sciences; MTdata, Meitu
  • Zhikang Fu MTdata, Meitu
  • Lili Zhao MTdata, Meitu

Keywords:

Feature Extraction, Action Recognition, Machine Learning

Abstract

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.

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Published

2018-04-27

How to Cite

Du, Y., Yuan, C., Li, B., Hu, W., Yang, H., Fu, Z., & Zhao, L. (2018). Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12248