Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Li Liu and Ling Shao and Xuelong Li and Ke Lu",
  journal =      "IEEE Transactions on Cybernetics",
  title =        "Learning Spatio-Temporal Representations for Action
                 Recognition: A Genetic Programming Approach",
  year =         "2016",
  volume =       "46",
  number =       "1",
  pages =        "158--170",
  keywords =     "genetic algorithms, genetic programming, Action
                 recognition, feature extraction, feature learning,
                 spatio-temporal descriptors",
  DOI =          "doi:10.1109/TCYB.2015.2399172",
  ISSN =         "2168-2267",
  abstract =     "Extracting discriminative and robust features from
                 video sequences is the first and most critical step in
                 human action recognition. In this paper, instead of
                 using handcrafted features, we automatically learn
                 spatio-temporal motion features for action recognition.
                 This is achieved via an evolutionary method, i.e.,
                 genetic programming (GP), which evolves the motion
                 feature descriptor on a population of primitive 3D
                 operators (e.g., 3D-Gabor and wavelet). In this way,
                 the scale and shift invariant features can be
                 effectively extracted from both colour and optical flow
                 sequences. We intend to learn data adaptive descriptors
                 for different datasets with multiple layers, which
                 makes fully use of the knowledge to mimic the physical
                 structure of the human visual cortex for action
                 recognition and simultaneously reduce the GP searching
                 space to effectively accelerate the convergence of
                 optimal solutions. In our evolutionary architecture,
                 the average cross-validation classification error,
                 which is calculated by an support-vector-machine
                 classifier on the training set, is adopted as the
                 evaluation criterion for the GP fitness function. After
                 the entire evolution procedure finishes, the
                 best-so-far solution selected by GP is regarded as the
                 (near-)optimal action descriptor obtained. The
                 GP-evolving feature extraction method is evaluated on
                 four popular action datasets, namely KTH, HMDB51, UCF
                 YouTube, and Hollywood2. Experimental results show that
                 our method significantly outperforms other types of
                 features, either hand-designed or machine-learnt.",
  notes =        "Also known as \cite{7042326}",

Genetic Programming entries for Li Liu Ling Shao Xuelong Li Ke Lu