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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

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

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

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


DOI:

10.1609/aaai.v32i1.12248


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.

Topics: AAAI

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HOW TO CITE:

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition AAAI 2018, .

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao (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, .

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. 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". .

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. (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, p.

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao, "Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition", AAAI, p., 2018.

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. "Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. "Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Yang Du||Chunfeng Yuan||Bing Li||Weiming Hu||Hao Yang||Zhikang Fu||Lili Zhao. Hierarchical Nonlinear Orthogonal Adaptive-Subspace Self-Organizing Map Based Feature Extraction for Human Action Recognition. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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