Sequential Compact Code Learning for Unsupervised Image Hashing

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Li Liu and Ling Shao",
  journal =      "IEEE Transactions on Neural Networks and Learning
  title =        "Sequential Compact Code Learning for Unsupervised
                 Image Hashing",
  year =         "2015",
  abstract =     "Effective hashing for large-scale image databases is a
                 popular research area, attracting much attention in
                 computer vision and visual information retrieval.
                 Several recent methods attempt to learn either graph
                 embedding or semantic coding for fast and accurate
                 applications. In this paper, a novel unsupervised
                 framework, termed evolutionary compact embedding (ECE),
                 is introduced to automatically learn the task-specific
                 binary hash codes. It can be regarded as an
                 optimisation algorithm that combines the genetic
                 programming (GP) and a boosting trick. In our
                 architecture, each bit of ECE is iteratively computed
                 using a weak binary classification function, which is
                 generated through GP evolving by jointly minimizing its
                 empirical risk with the AdaBoost strategy on a training
                 set. We address this as greedy optimisation by
                 embedding high-dimensional data points into a
                 similarity-preserved Hamming space with a low
                 dimension. We systematically evaluate ECE on two data
                 sets, SIFT 1M and GIST 1M, showing the effectiveness
                 and the accuracy of our method for a large-scale
                 similarity search.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TNNLS.2015.2495345",
  ISSN =         "2162-237X",
  notes =        "Also known as \cite{7323857}",

Genetic Programming entries for Li Liu Ling Shao