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A Novel Method for Near-Duplicate Image Detection Using Global Features

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

The rapid growth of digital multimedia content has led to an increase in near-duplicate images. Near-duplicate image detection is a critical task in the field of multimedia forensics, which aims to detect and identify illegally distributed copies of an original image. Gaussian Hermite Moments (GHM) have been proven to be an effective global feature for image representation and analysis in various computer vision tasks, including image forgery detection. In this paper, we propose a novel near-duplicate image detection method that utilizes GHM as the global feature descriptor. We conducted experiments on the CoMoFoD image dataset. The experimental results demonstrate that the proposed method outperforms existing methods for near-duplicate image detection under various post-processing operations and geometric transformations, particularly scaling and rotation. Moreover, the proposed method is significantly faster than existing methods for near-duplicate image detection and it can potentially make the proposed method more practical for real-world applications.

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Author information

Authors and Affiliations

  1. Jaypee University of Engineering and Technology, Raghogarh, Guna – MP, India

    Kunj Bihari Meena, Vipin Tyagi & Vipin Tyagi

Authors
  1. Kunj Bihari Meena
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  2. Vipin Tyagi
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Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Meena, K.B., Tyagi, V. (2023). A Novel Method for Near-Duplicate Image Detection Using Global Features. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37939-0

  • Online ISBN: 978-3-031-37940-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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