Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval

Xing Tian, Xiancheng Zhou, Wing Ng, Jiayong Li, Hui Wang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
130 Downloads (Pure)


With the rapid growth of multimedia data on the Internet, content-based image retrieval becomes a key technique for the Internet development. Hashing methods are efficient and effective for image retrieval. Dual Complementary Hashing (DCH) is one such method, which uses multiple hash tables and has good performance. However, DCH utilizes wrongly hashed image pairs to train the following hash table and discards correctly hashed image pairs. Therefore, the number of image pairs utilized for training the following hash tables will decrease rapidly. Moreover, each hash function in a hash table of DCH is trained by correcting the errors caused by its preceding one instead of holistically considering errors made by all previous hash functions. These restrictions significantly reduce the training efficiency and the overall performance of DCH. In this paper, we propose a new hashing method for image retrieval, Bootstrap Dual Complementary Hashing with semi-supervised Re-ranking (BDCHR). It is a semi-supervised multi-hashing method consisting of two parts: bootstrap DCH and semi-supervised re-ranking. The first part relieves the restrictions of DCH while the second part further enhances the image retrieval performance. Experimental results show that BDCHR yields better performance than other state-of-the-art multi-hashing methods.

Original languageEnglish
Pages (from-to)103-116
Number of pages14
Early online date31 Oct 2019
Publication statusPublished (in print/issue) - 28 Feb 2020


  • Multi-hashing
  • dual complementary hashing
  • image retrieval
  • semi-supervised


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