Sequential compact code learning for unsupervised image hashing

Liu, Li and Shao, Ling (2016) Sequential compact code learning for unsupervised image hashing. IEEE Transactions on Neural Networks and Learning Systems, 27 (12). pp. 2526-2536. ISSN 2162-237X

[thumbnail of Accepted manuscript]
PDF (Accepted manuscript) - Accepted Version
Download (1MB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: unsupervised,adaboost,binary hash codes,genetic programming (gp),large-scale similarity search
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 10 Mar 2017 01:41
Last Modified: 22 Oct 2022 02:10
DOI: 10.1109/TNNLS.2015.2495345


Downloads per month over past year

Actions (login required)

View Item View Item