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Multi-feature Fusion-Based Central Similarity Deep Supervised Hashing

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14431))

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Abstract

The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Nevertheless, previous deep supervised hashing methods merely focus on the high-level features of the image and neglect the low-level features of the image. Low-level features usually contain more detailed information. Therefore, we propose a multi-feature fusion-based central similarity deep supervised hashing method. Specifically, a cross-layer fusion module is designed to effectively fuse image features of high and low levels. On top of that, a channel attention module is introduced to filter out the useless information in the fused features. We perform comprehensive experiments on three widely-studied datasets: NUS-WIDE, MS-COCO and ImageNet. Experimental results indicate that our proposed method has superior performance compared to state-of-the-art deep supervised hashing methods.

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Acknowledgments

This study is supported by the Natural Science Foundation of Inner Mongolia Autonomous Region under Grant 2019ZD14, the Project for Science and Technology of Inner Mongolia Autonomous Region under Grant 2019GG281, and the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region under Grant NJYT-20-A05.

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Authors and Affiliations

  1. School of Computer Science, Inner Mongolia University, Hohhot, China

    Chao He, Hongxi Wei & Kai Lu

  2. Provincial Key Laboratory of Mongolian Information Processing Technology, Hohhot, China

    Chao He, Hongxi Wei & Kai Lu

  3. National and Local Joint Engineering Research Center of Mongolian Information Processing Technology, Hohhot, China

    Chao He, Hongxi Wei & Kai Lu

Authors
  1. Chao He
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  2. Hongxi Wei
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  3. Kai Lu
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Corresponding author

Correspondence to Hongxi Wei .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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He, C., Wei, H., Lu, K. (2024). Multi-feature Fusion-Based Central Similarity Deep Supervised Hashing. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_27

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  • DOI: https://doi.org/10.1007/978-981-99-8540-1_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8539-5

  • Online ISBN: 978-981-99-8540-1

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