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Effective human action recognition by combining manifold regularization and pairwise constraints

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Abstract

The ever-growing popularity of mobile networks and electronics has prompted intensive research on multimedia data (e.g. text, image, video, audio, etc.) management. This leads to the researches of semi-supervised learning that can incorporate a small number of labeled and a large number of unlabeled data by exploiting the local structure of data distribution. Manifold regularization and pairwise constraints are representative semi-supervised learning methods. In this paper, we introduce a novel local structure preserving approach by considering both manifold regularization and pairwise constraints. Specifically, we construct a new graph Laplacian that takes advantage of pairwise constraints compared with the traditional Laplacian. The proposed graph Laplacian can better preserve the local geometry of data distribution and achieve the effective recognition. Upon this, we build the graph regularized classifiers including support vector machines and kernel least squares as special cases for action recognition. Experimental results on a multimodal human action database (CAS-YNU-MHAD) show that our proposed algorithms outperform the general algorithms.

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Acknowledgements

This paper is partly supported by the National Natural Science Foundation of China (Grant No. 61671480), the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 14CX02203A, YCX2017059).

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Authors and Affiliations

  1. China University of Petroleum (East China), Qingdao, 266580, China

    Xueqi Ma & Weifeng Liu

  2. Yunnan University, Kunming, 650091, China

    Dapeng Tao

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  1. Xueqi Ma
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  2. Dapeng Tao
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  3. Weifeng Liu
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Correspondence to Weifeng Liu.

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Ma, X., Tao, D. & Liu, W. Effective human action recognition by combining manifold regularization and pairwise constraints. Multimed Tools Appl 78, 13313–13329 (2019). https://doi.org/10.1007/s11042-017-5172-1

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  • DOI: https://doi.org/10.1007/s11042-017-5172-1

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