Feature Learning for Image Classification via Multiobjective Genetic Programming

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@Article{Shao:2014:ieeeNNLS,
  author =       "Ling Shao and Li Liu and Xuelong Li",
  journal =      "IEEE Transactions on Neural Networks and Learning
                 Systems",
  title =        "Feature Learning for Image Classification via
                 Multiobjective Genetic Programming",
  year =         "2014",
  month =        jul,
  volume =       "25",
  number =       "7",
  pages =        "1359--1371",
  keywords =     "genetic algorithms, genetic programming, Feature
                 extraction, image classification, multiobjective
                 optimisation.",
  DOI =          "doi:10.1109/TNNLS.2013.2293418",
  ISSN =         "2162-237X",
  size =         "13 pages",
  abstract =     "Feature extraction is the first and most critical step
                 in image classification. Most existing image
                 classification methods use hand-crafted features, which
                 are not adaptive for different image domains. In this
                 paper, we develop an evolutionary learning methodology
                 to automatically generate domain-adaptive global
                 feature descriptors for image classification using
                 multiobjective genetic programming (MOGP). In our
                 architecture, a set of primitive 2-D operators are
                 randomly combined to construct feature descriptors
                 through the MOGP evolving and then evaluated by two
                 objective fitness criteria, i.e., the classification
                 error and the tree complexity. After the entire
                 evolution procedure finishes, the best-so-far solution
                 selected by the MOGP is regarded as the (near-)optimal
                 feature descriptor obtained. To evaluate its
                 performance, the proposed approach is systematically
                 tested on the Caltech-101, the MIT urban and nature
                 scene, the CMU PIE, and Jochen Triesch Static Hand
                 Posture II data sets, respectively. Experimental
                 results verify that our method significantly
                 outperforms many state-of-the-art hand-designed
                 features and two feature learning techniques in terms
                 of classification accuracy.",
  notes =        "Also known as \cite{6683022}",
}

Genetic Programming entries for Ling Shao Li Liu Xuelong Li

Citations