Semi-Supervised Multi-View Discrete Hashing for Fast Image Search

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Abstract
Hashing is an important method for fast neighbor search on large scale dataset in Hamming space. While most research on hash models are focusing on single-view data, recently the multi-view approaches with a majority of unsupervised multi-view hash models have been considered. Despite of existence of millions of unlabeled data samples, it is believed that labeling a handful of data will remarkably improve the searching performance. In this paper, we propose a semi-supervised multi-view hash model. Besides incorporating a portion of label information into the model, the proposed multi-view model differs from existing multi-view hash models in three-fold: 1) a composite discrete hash learning modeling that is able to minimize the loss jointly on multi-view features when using relaxation on learning hashing codes; 2) exploring statistically uncorrelated multi-view features for generating hash codes; and 3) a composite locality preserving modeling for locally compact coding. Extensive experiments have been conducted to show the effectiveness of the proposed semi-supervised multi-view hash model as compared with related multi-view hash models and semi-supervised hash models.
Funding Information
  • NSFC (61522115, 61472456, 61661130157)
  • RS-Newton Advanced Fellowship (NA150459)
  • Guangdong Natural Science Funds for Distinguished Young Scholar (S2013050014265)
  • GuangDong Program (2015B010105005)
  • Guangdong Science and Technology Planning Project (2016A010102012, 2014B010118003)
  • Guangdong Program for Support of Top-notch Young Professionals (2014T Q01X779)