Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval

Guanghua GU; Wenhua HUO; Mingyue SU; Hao FU

doi:10.11999/JEIT200988

Volume 43 Issue 12

Dec. 2021

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Guanghua GU, Wenhua HUO, Mingyue SU, Hao FU. Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3530-3537. doi: 10.11999/JEIT200988

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Guanghua GU, Wenhua HUO, Mingyue SU, Hao FU. Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3530-3537. doi: 10.11999/JEIT200988

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Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval

doi: 10.11999/JEIT200988

1.
School of Information Science and Engineering, Yanshan Engineering University, Qinhuangdao 066000, China
2.
Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066000, China

Funds: The National Natural Science Foundation of China (62072394), The Natural Science Foundation of Hebei Province (F2021203019)

Received Date: 2020-11-23
Accepted Date: 2021-11-05
Rev Recd Date: 2021-10-25

Available Online: 2021-11-09

Publish Date: 2021-12-21

Abstract

Abstract

Hashing is widely used for image retrieval tasks. In view of the limitations of existing deep supervised hashing methods, a new Asymmetric Supervised Deep Discrete Hashing (ASDDH) method is proposed to maintain the semantic structure between different categories and generate binary codes. Firstly, a deep network is used to extract image features and reveal the similarity between each pair of images according to their semantic labels. To enhance the similarity between binary codes and ensure the retention of multi-label semantics, this paper designs an asymmetric hashing method that utilizes a multi-label binary code mapping to make the hash codes have multi-label semantic information. In addition, the bit balance of the binary code is introduced to balance each bit, which encourages the number of -1 and +1 to be approximately similar among all training samples. Experimental results on two benchmark datasets show that the proposed method is superior to other methods in image retrieval.
- Image retrieval,
- Supervised hashing,
- Semantic preservation,
- Deep learning

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References(27)

References

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