Paper Info
Title
Bootstrap dual complementary hashing with semi-supervised re-ranking for image retrieval
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.10.073
Neurocomputing
Keywords
DocType
Volume
Multi-hashing,dual complementary hashing,image retrieval,semi-supervised
Journal
379
ISSN
Citations 
PageRank 
0925-2312
2
0.35
References 
Authors
0
5
Name
Order
Citations
PageRank
Xing Tian1163.27
Xiancheng Zhou281.44
Wing W. Y. Ng352856.12
Jiayong Li431.38
Hui Wang525724.41