Sequential compact code learning for unsupervised image hashing

Li Liu, Ling Shao

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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 optimization 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 optimization 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.
Original languageEnglish
Pages (from-to)2526-2536
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Early online date10 Nov 2015
Publication statusPublished - 1 Dec 2016


  • unsupervised
  • AdaBoost
  • binary hash codes
  • genetic programming (GP)
  • large-scale similarity search

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