Fast and Multilevel Semantic-Preserving Discrete Hashing

Wanqian Zhang (Chinese Academy of Sciences), Dayan Wu (Chinese Academy of Sciences), Jing Liu (Chinese Academy of Sciences), Bo Li (Chinese Academy of Sciences), Xiaoyan Gu (Chinese Academy of Sciences), Weiping Wang (Chinese Academy of Sciences), Dan Meng (Chinese Academy of Sciences)

Abstract
Deep hashing methods have achieved great success in multi-label image retrieval due to its computation and storage efficiency. However, most existing methods adopt a relaxation-and-quantization optimization strategy, which inevitably degrades the performance. Besides, existing discrete hashing methods are very time-consuming because of the bit-wise learning strategy. To tackle these issues, we propose a novel deep asymmetric discrete hashing method, called Fast and Multilevel semantic-preserving Discrete Hashing (FMDH). FMDH makes the best of supervised information to preserve the multilevel semantic similarities between multi-label images, and further accelerates the training process. Extensive experiments on two widely used multi-label image datasets demonstrate that FMDH can achieve the state-of-the-art performance on both accuracy and training time efficiency.

DOI
10.5244/C.33.156
https://dx.doi.org/10.5244/C.33.156

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