Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction

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@InProceedings{Al-Sahaf:2015:GECCO,
  author =       "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
  title =        "Evolutionary Image Descriptor: A Dynamic Genetic
                 Programming Representation for Feature Extraction",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "975--982",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754661",
  DOI =          "doi:10.1145/2739480.2754661",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Texture classification aims at categorising instances
                 that have a similar repetitive pattern. In computer
                 vision, texture classification represents a fundamental
                 element in a wide variety of applications, which can be
                 performed by detecting texture primitives of the
                 different classes. Using image descriptors to detect
                 prominent features has been widely adopted in computer
                 vision. Building an effective descriptor becomes more
                 challenging when there are only a few labelled
                 instances. This paper proposes a new Genetic
                 Programming (GP) representation for evolving an image
                 descriptor that operates directly on the raw pixel
                 values and uses only two instances per class. The new
                 method synthesises a set of mathematical formulas that
                 are used to generate the feature vector, and the
                 classification is then performed using a simple
                 instance-based classifier. Determining the length of
                 the feature vector is automatically handled by the new
                 method. Two GP and nine well-known non-GP methods are
                 compared on two texture image data sets for texture
                 classification in order to test the effectiveness of
                 the proposed method. The proposed method is also
                 compared to three hand-crafted descriptors namely
                 domain-independent features, local binary patterns, and
                 Haralick texture features. The results show that the
                 proposed method has superior performance over the
                 competitive methods.",
  notes =        "Also known as \cite{2754661} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",
}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

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