Extracting Image Features for Classification By Two-Tier Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Al-Sahaf:2012:CEC,
  title =        "Extracting Image Features for Classification By
                 Two-Tier Genetic Programming",
  author =       "Harith Al-Sahaf and Andy Song and 
                 Kourosh Neshatian and Mengjie Zhang",
  pages =        "1630--1637",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256412",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computer Vision",
  abstract =     "Image classification is a complex but important task
                 especially in the areas of machine vision and image
                 analysis such as remote sensing and face recognition.
                 One of the challenges in image classification is
                 finding an optimal set of features for a particular
                 task because the choice of features has direct impact
                 on the classification performance. However the goodness
                 of a feature is highly problem dependent and often
                 domain knowledge is required. To address these issues
                 we introduce a Genetic Programming (GP) based image
                 classification method, Two-Tier GP, which directly
                 operates on raw pixels rather than features. The first
                 tier in a classifier is for automatically defining
                 features based on raw image input, while the second
                 tier makes decision. Compared to conventional feature
                 based image classification methods, Two-Tier GP
                 achieved better accuracies on a range of different
                 tasks. Furthermore by using the features defined by the
                 first tier of these Two-Tier GP classifiers,
                 conventional classification methods obtained higher
                 accuracies than classifying on manually designed
                 features. Analysis on evolved Two-Tier image
                 classifiers shows that there are genuine features
                 captured in the programs and the mechanism of achieving
                 high accuracy can be revealed. The Two-Tier GP method
                 has clear advantages in image classification, such as
                 high accuracy, good interpretability and the removal of
                 explicit feature extraction process.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Harith Al-Sahaf Andy Song Kourosh Neshatian Mengjie Zhang

Citations