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A New Bilinear Supervised Neighborhood Discrete Discriminant Hashing

Author

Listed:
  • Xueyu Chen

    (School of Information Engineering, Nanjing Audit University, Nanjing 211815, China)

  • Minghua Wan

    (School of Information Engineering, Nanjing Audit University, Nanjing 211815, China
    Key Laboratory of Intelligent Information processing, Nanjing Xiaozhuang University, Nanjing 211171, China
    Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210014, China)

  • Hao Zheng

    (Key Laboratory of Intelligent Information processing, Nanjing Xiaozhuang University, Nanjing 211171, China)

  • Chao Xu

    (School of Information Engineering, Nanjing Audit University, Nanjing 211815, China)

  • Chengli Sun

    (School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China)

  • Zizhu Fan

    (School of Science, East China Jiaotong University, Nanchang 330013, China)

Abstract

Feature extraction is an important part of perceptual hashing. How to compress the robust features of images into hash codes has become a hot research topic. Converting a two-dimensional image into a one-dimensional descriptor requires a higher computational cost and is not optimal. In order to maintain the internal feature structure of the original two-dimensional image, a new Bilinear Supervised Neighborhood Discrete Discriminant Hashing (BNDDH) algorithm is proposed in this paper. Firstly, the algorithm constructs two new neighborhood graphs to maintain the geometric relationship between samples and reduces the quantization loss by directly constraining the hash codes. Secondly, two small rotation matrices are used to realize the bilinear projection of the two-dimensional descriptor. Finally, the experiment verifies the performance of the BNDDH algorithm under different feature types, such as image original pixels and a Convolutional Neural Network (CNN)-based AlexConv5 feature. The experimental results and discussion clearly show that the proposed BNDDH algorithm is better than the existing traditional hashing algorithm and can represent the image more efficiently in this paper.

Suggested Citation

  • Xueyu Chen & Minghua Wan & Hao Zheng & Chao Xu & Chengli Sun & Zizhu Fan, 2022. "A New Bilinear Supervised Neighborhood Discrete Discriminant Hashing," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2110-:d:841338
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    References listed on IDEAS

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    1. Ruba Abu Khurma & Ibrahim Aljarah & Ahmad Sharieh & Mohamed Abd Elaziz & Robertas Damaševičius & Tomas Krilavičius, 2022. "A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem," Mathematics, MDPI, vol. 10(3), pages 1-45, January.
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    Cited by:

    1. Guowei Yang & Lin Zhang & Minghua Wan, 2022. "Exponential Graph Regularized Non-Negative Low-Rank Factorization for Robust Latent Representation," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    2. Minghua Wan & Xichen Wang & Hai Tan & Guowei Yang, 2022. "Manifold Regularized Principal Component Analysis Method Using L2,p-Norm," Mathematics, MDPI, vol. 10(23), pages 1-17, December.

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