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Attribute-based supervised deep learning model for action recognition

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Abstract

Deep learning has been the most popular feature learning method used for a variety of computer vision applications in the past 3 years. Not surprisingly, this technique, especially the convolutional neural networks (ConvNets) structure, is exploited to identify the human actions, achieving great success. Most algorithms in existence directly adopt the basic ConvNets structure, which works pretty well in the ideal situation, e.g., under stable lighting conditions. However, its performance degrades significantly when the intra-variation in relation to image appearance occurs within the same category. To solve this problem, we propose a new method, integrating the semantically meaningful attributes into deep learning’s hierarchical structure. Basically, the idea is to add simple yet effective attributes to the category level of ConvNets such that the attribute information is able to drive the learning procedure. The experimental results based on three popular action recognition databases show that the embedding of auxiliary multiple attributes into the deep learning framework improves the classification accuracy significantly.

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Author information

Authors and Affiliations

  1. School of Software, Tsinghua University, Beijing, 100084, China

    Kai Chen & Guiguang Ding

  2. Department of Computer Science, Northumbria University, Newcastle, NE1 8ST, UK

    Jungong Han

Authors
  1. Kai Chen
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  2. Guiguang Ding
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  3. Jungong Han
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Corresponding author

Correspondence to Guiguang Ding.

Additional information

Kai Chen received the BS degree from the School of Software, Tsinghua University, China in 2014, where he is currently pursuing the MS degree with the School of Software. His research interests include multimedia information retrieval, computer vision, and machine learning.

Guiguang Ding received the PhD degree in electronic engineering from Xidian University, China. He is currently an associate professor with the School of Software, Tsinghua University, China. His current research focuses on the area of multimedia information retrieval and management, in particular, visual object classification, automatic semantic annotation, content-based multimedia indexing, social multimedia retrieval, mining and recommendation. He has published about 40 research papers in international conferences and journals and applied for eight Patent Rights in China.

Jungong Han is a senior lecturer with the Department of Computer Science at Northumbria University, UK. Previously, he was a senior scientist (2012–2015) with Civolution Technology (a combining synergy of Philips CI and Thomson STS), a research staff (2010–2012) with the Centre forMathematics and Computer Science, and a researcher (2005–2010) with the Technical University of Eindhoven in Netherlands. Dr. Han’s research interests include multimedia content identification, computer vision, and artificial intelligence. He has written and co-authored over 100 papers, in which one first-authored paper has been cited, up to date, for more than 500 times. He is an associate editor of Elsevier Neurocomputing and Springer Multimedia Tools and Applications.

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Chen, K., Ding, G. & Han, J. Attribute-based supervised deep learning model for action recognition. Front. Comput. Sci. 11, 219–229 (2017). https://doi.org/10.1007/s11704-016-6066-5

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