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Spatially regularized and locality-constrained linear coding for human action recognition

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Abstract

To reduce quantization error, preserve the manifold of local features, distinguish the ambiguous features, and model the spatial configuration of features for Bag-of-Features (BoF) model-based human action recognition, a novel feature coding method called spatially regularized and locality-constrained linear coding (SLLC) is proposed. The spatial regularization and locality constraint are involved in the feature coding phase to model the spatial configuration of features and preserve their nonlinear manifold. The action recognition experimental results on benchmark datasets show that SLLC achieves better performance than the state-of-the-art feature coding methods such as soft vector quantization, sparse coding, and locality-constrained linear coding.

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Authors and Affiliations

  1. Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, P. R. China

    Bin Wang, Wen Gai & Shouchun Guo

  2. College of Information System and Management, National University of Defense Technology, Changsha, 410073, P. R. China

    Yu Liu, Wei Wang & Maojun Zhang

Authors
  1. Bin Wang
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  2. Wen Gai
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  3. Shouchun Guo
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  4. Yu Liu
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  5. Wei Wang
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  6. Maojun Zhang
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Correspondence to Bin Wang.

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Wang, B., Gai, W., Guo, S. et al. Spatially regularized and locality-constrained linear coding for human action recognition. OPT REV 21, 226–236 (2014). https://doi.org/10.1007/s10043-014-0033-x

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