Abstract
Deep hashing can improve computational efficiency and save storage space, which is the most significant part of image retrieval task and has received extensive research attention. Existing deep hashing frameworks mainly fall into two categories: single-stage and two-stage. For multi-label image retrieval, most single-stage and two-stage deep hashing methods usually consider two images to be similar if one pair of the corresponding category labels is the same, and do not make full use of the multi-label information. Meanwhile, some novel two-stage deep hashing methods proposed in recent years construct hash centers firstly and then train through deep neural networks. For multi-label processing, these two-stage methods usually convert multi-label into single-label objective, which also leads to insufficient use of label information. In this paper, a novel multi-label deep hashing method is proposed by constructing the similarity matrix and designing the optimization algorithm to construct the hash centers, and the proposed method constructs the training loss function through the multi-label hash centers constraint and anchor constraint of center pairs. Experiments on several multi-label image benchmark datasets show that the proposed method can achieve the state-of-the-art results.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61772567, U1811262, U1911203, U2001211, U22B2060), Guangdong Basic and Applied Basic Research Foundation (2019B1515130001, 2021A1515012172, 2023A1515011400), Key-Area Research and Development Program of Guangdong Province (2020B0101100001).
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Liu, Y., Pan, Y., Yin, J. (2024). Deep Hashing for Multi-label Image Retrieval with Similarity Matrix Optimization of Hash Centers and Anchor Constraint of Center Pairs. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_3
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