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Length adaptive hashing for semi-supervised semantic image retrieval

  • 1227: Content-based Image Retrieval
  • Published:
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Abstract

Image Hashing methods have proven to be both effective and efficient for large-scale image retrieval problem. The advances in hashing methods concentrate on the learning of image features and hash tables. Existing hashing methods manually select fixed hash code length for all classes of images in a large database. However, we have observed that the length of the hash code is essential to the retrieval performance but it is rarely studied. Short hash codes cannot preserve similarity among images well while long hash codes may lead to high storage costs. Linear search for the optimal length of hash code length is time-consuming. In this paper, a semi-supervised length adaptive hashing method (LAH) is proposed to adaptively optimize hash code lengths for different semantic image classes using a multiobjective evolutionary algorithm based on decomposition. Two objectives regarding retrieval precision and storage cost are set for optimization. We conduct experiments on three real-world image databases and the experimental results show that the proposed LAH significantly improves the retrieval performance compared to the original traditional semi-supervised hashing methods.

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Data Availability

The datasets analysed during the current study of this article will be made available by the authors, without undue reservation.

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Acknowledgements

This work is supported in part by the Guangdong Natural Science Funds for Distinguished Young Scholars under Grant 2022B1515020049, in part by the Guangdong Regional Joint Fund for Basic and Applied Research under Grant 2021B1515120078.

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    1. School of Computer Science and Engineering, South China Univerisity of Technology, Guangzhou, China

      Si-chao Lei, Xing Tian, Wing W.Y. Ng & Yue-Jiao Gong

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    Correspondence to Si-chao Lei.

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    Lei, Sc., Tian, X., Ng, W.W. et al. Length adaptive hashing for semi-supervised semantic image retrieval. Multimed Tools Appl 82, 38165–38187 (2023). https://doi.org/10.1007/s11042-023-14377-2

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