Evolutionary compact embedding for large-scale image classification

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@Article{Liu:2014:IS,
  author =       "Li Liu and Ling Shao and Xuelong Li",
  title =        "Evolutionary compact embedding for large-scale image
                 classification",
  journal =      "Information Sciences",
  volume =       "316",
  pages =        "567--581",
  year =         "2015",
  month =        "20 " # sep,
  keywords =     "genetic algorithms, genetic programming,
                 Dimensionality reduction, Large-scale image
                 classification, Evolutionary compact embedding,
                 AdaBoost",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2014.06.030",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025514006586",
  abstract =     "Effective dimensionality reduction is a classical
                 research area for many large-scale analysis tasks in
                 computer vision. Several recent methods attempt to
                 learn either graph embedding or binary hashing for fast
                 and accurate applications. In this paper, we propose a
                 novel framework to automatically learn the
                 task-specific compact coding, called evolutionary
                 compact embedding (ECE), which can be regarded as an
                 optimisation algorithm combining genetic programming
                 (GP) and a boosting trick. As an evolutionary
                 computation methodology, GP can solve problems inspired
                 by natural evolution without any prior knowledge of the
                 solutions. In our evolutionary architecture, each bit
                 of ECE is iteratively computed using a binary
                 classification function, which is generated through GP
                 evolving by jointly minimising its empirical risk with
                 the AdaBoost strategy on a training set. We address
                 this as greedy optimisation leading to small Hamming
                 distances for similar samples and large distances for
                 dissimilar samples. We then evaluate ECE on four image
                 datasets: USPS digital hand-writing, CMU PIE face,
                 CIFAR-10 tiny image and SUN397 scene, showing the
                 accurate and robust performance of our method for
                 large-scale image classification.",
}

Genetic Programming entries for Li Liu Ling Shao Xuelong Li

Citations