An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Ying Bi and Bing Xue and Mengjie Zhang",
  title =        "An Automatic Feature Extraction Approach to Image
                 Classification Using Genetic Programming",
  booktitle =    "21st International Conference on the Applications of
                 Evolutionary Computation, EvoIASP 2018",
  year =         "2018",
  editor =       "Stefano Cagnoni and Mengjie Zhang",
  series =       "LNCS",
  volume =       "10784",
  publisher =    "Springer",
  pages =        "421-–438",
  address =      "Parma, Italy",
  month =        "4-6 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, Image
                 classification, Feature extraction, Image analysis",
  isbn13 =       "978-3-319-77537-1",
  DOI =          "doi:10.1007/978-3-319-77538-8_29",
  abstract =     "Feature extraction is an essential process for image
                 data dimensionality reduction and classification.
                 However, feature extraction is very difficult and often
                 requires human intervention. Genetic Programming (GP)
                 can achieve automatic feature extraction and image
                 classification but the majority of existing methods
                 extract low-level features from raw images without any
                 image-related operations. Furthermore, the work on the
                 combination of image-related operators/descriptors in
                 GP for feature extraction and image classification is
                 limited. This paper proposes a multi-layer GP approach
                 (MLGP) to performing automatic high-level feature
                 extraction and classification. A new program structure,
                 a new function set including a number of image
                 operators/descriptors and two region detectors, and a
                 new terminal set are designed in this approach. The
                 performance of the proposed method is examined on six
                 different data sets of varying difficulty and compared
                 with five GP based methods and 42 traditional image
                 classification methods. Experimental results show that
                 the proposed method achieves better or comparable
                 performance than these baseline methods. Further
                 analysis on the example programs evolved by the
                 proposed MLGP method reveals the good interpretability
                 of MLGP and gives insight into how this method can
                 effectively extract high-level features for image
  notes =        "EvoApplications2018 held in conjunction with
                 EuroGP'2018 EvoCOP2018 and EvoMusArt2018

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang