Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

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@Article{Al-Sahaf:2017a:ieeeTEC,
  author =       "Harith Al-Sahaf and Ausama Al-Sahaf and Bing Xue and 
                 Mark Johnston and Mengjie Zhang",
  title =        "Automatically Evolving Rotation-Invariant Texture
                 Image Descriptors by Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2017",
  volume =       "21",
  number =       "1",
  pages =        "83--101",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming,
                 Classification, feature extraction, image descriptor,
                 keypoint detection",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2016.2577548",
  size =         "19 pages",
  abstract =     "In computer vision, training a model that performs
                 classification effectively is highly dependent on the
                 extracted features, and the number of training
                 instances. Conventionally, feature detection and
                 extraction are performed by a domain expert who, in
                 many cases, is expensive to employ and hard to find.
                 Therefore, image descriptors have emerged to automate
                 these tasks. However, designing an image descriptor
                 still requires domain-expert intervention. Moreover,
                 the majority of machine learning algorithms require a
                 large number of training examples to perform well.
                 However, labelled data is not always available or easy
                 to acquire, and dealing with a large dataset can
                 dramatically slow down the training process. In this
                 paper, we propose a novel genetic programming-based
                 method that automatically synthesises a descriptor
                 using only two training instances per class. The
                 proposed method combines arithmetic operators to evolve
                 a model that takes an image and generates a feature
                 vector. The performance of the proposed method is
                 assessed using six datasets for texture classification
                 with different degrees of rotation and is compared with
                 seven domain-expert designed descriptors. The results
                 show that the proposed method is robust to rotation and
                 has significantly outperformed, or achieved a
                 comparable performance to, the baseline methods.",
  notes =        "also known as \cite{7486119}",
}

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

Citations