Bootstrap dual complementary hashing for large scale image retrieval

Image retrieval is one of important applications in big data environments. Hashing methods have been widely used to deal with large scale image retrieval problems because of its sublinear time complexities. Hashing methods with multiple hash tables achieve high precision and recall rates using fewer hash bucket visits in comparison to single-table-based hashing. The Dual Complementary Hashing (DCH) is one of the state-of-the-art multi-hashing methods which compensate error made by both previous tables and bits when constructing new one in the training process. However, after a few hash tables are created, the DCH fails to improve performance by creating new hash tables. This is because correctly learned images are ignored in new table training which leads to a lack of training samples after a few iterations. In this paper, the Bootstrap DCH (BDCH) is proposed to relieve this problem. Experimental results on three databases show that the BDCH outperforms the DCH and several hashing methods.