Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors

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@Article{Al-Sahaf:2017:ieeeTEC,
  author =       "Harith Al-Sahaf and Mengjie Zhang and 
                 Ausama Al-Sahaf and Mark Johnston",
  journal =      "IEEE Transactions on Evolutionary Computation",
  title =        "Keypoints Detection and Feature Extraction: A Dynamic
                 Genetic Programming Approach for Evolving
                 Rotation-invariant Texture Image Descriptors",
  year =         "2017",
  abstract =     "The goodness of the features extracted from the
                 instances and the number of training instances are two
                 key components in machine learning, and building an
                 effective model is largely affected by these two
                 factors. Acquiring a large number of training instances
                 is very expensive in some situations such as in the
                 medical domain. Designing a good feature set, on the
                 other hand, is very hard and often requires domain
                 expertise. In computer vision, image descriptors have
                 emerged to automate feature detection and extraction;
                 however, domain-expert intervention is typically needed
                 to develop these descriptors. The aim of this paper is
                 to use Genetic Programming to automatically construct a
                 rotation-invariant image descriptor by synthesising a
                 set of formulae using simple arithmetic operators and
                 first-order statistics, and determining the length of
                 the feature vector simultaneously using only two
                 instances per class. Using seven texture classification
                 image datasets, the performance of the proposed method
                 is evaluated and compared against eight domain-expert
                 hand-crafted image descriptors. Quantitatively, the
                 proposed method has significantly outperformed, or
                 achieved comparable performance to, the competitor
                 methods. Qualitatively, the analysis shows that the
                 descriptors evolved by the proposed method can be
                 interpreted.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TEVC.2017.2685639",
  ISSN =         "1089-778X",
  notes =        "Also known as \cite{7885048}",
}

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

Citations