Deep Supervised Hashing with Dynamic Weighting Scheme
Yingxiang Sun, Shuying Yu · 2020
Similarity preserving hashing methods have drawn a lot of attention lately due to their efficiency in performing approximate nearest neighbor searches on high-dimensional large-scale multimedia data. With the development of deep learning techniques, deep supervised hashing has attracted increasing attention lately. Existing deep hashing methods usually take a two-step strategy to generate hash codes. They generate continuous hash codes and then discretize them into binary hash codes. Deep hashing methods have outperformed other methods significantly, but there is still a huge quantization gap between the discrete hash codes and continuous hash codes. The quantization gap prevents existing deep hashing methods from achieving better performance. In this paper, we propose deep supervised hashing with dynamic weighting scheme (DHDW), a novel deep hashing method that narrows down the quantization gap by utilizing Hamming distance information during training. Specifically, we introduce Hamming distance and stability information into the training phase by applying a novel dynamic weighting module to all data of a mini-batch. The dynamic weighting module we present is constructed from real-time Hamming distance and stability information. Additionally, the dynamic weighting module is compatible with all existing pairwise deep supervised hashing methods. Comprehensive experiments show that DHDW outperforms existing state-of-the-art methods on several standard datasets and benchmarks, and the dynamic weighting module effectively improves the performance of deep learning to hash methods.