Genetic prOgramming for image feature descriptor learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{price:2017:CEC,
  author =       "Stanton R. Price and Derek T. Anderson",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Genetic prOgramming for image feature descriptor
                 learning",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "854--860",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "It is widely accepted that feature extraction is quite
                 possibly the most critical step in computer vision.
                 Typically, feature extraction is performed using a
                 method such as the histogram of oriented gradients. In
                 recent years, a shift has occurred from human to
                 machine learned features, e.g., convolutional neural
                 networks (CNNs) and Evolution-Constructed (ECO)
                 features. An advantage of our improved ECO (iECO)
                 framework is it optimizes features on a per-descriptor
                 basis. Herein, iECO is extended in order to represent a
                 richer class of features, namely arithmetic
                 combinations and compositions of iECOs. This extension,
                 called Genetic programming Optimal Feature Descriptor
                 (GOOFeD) is based on genetic programming (GP). Three
                 experiments are performed on data from a U.S. Army test
                 site that contains multiple target and clutter types,
                 burial depths, and times of day for automatic buried
                 explosive hazard detection. The first two experiments
                 focus on GOOFeD initialization and parameter selection.
                 The last experiment demonstrates that GOOFeD is
                 superior to iECO in terms of the fitness of evolved
                 individuals.",
  keywords =     "genetic algorithms, genetic programming, computer
                 vision, feature extraction, CNN, ECO features, GOOFeD
                 initialization, GP, Genetic programming Optimal Feature
                 Descriptor, automatic buried explosive hazard
                 detection, burial depths, convolutional neural
                 networks, evolution-constructed features, histogram of
                 oriented gradients, iECO framework, image feature
                 descriptor learning, parameter selection, Computers,
                 Histograms, Transforms, feature learning, image
                 processing",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969398",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969398}",
}

Genetic Programming entries for Stanton R Price Derek T Anderson

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