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Squirrel Search Optimization-based near-duplicate image detection.

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Title: Squirrel Search Optimization-based near-duplicate image detection.
Authors: Sundaram, Srinidhi1 (AUTHOR) srinidhisristar@gmail.com, Somasundaram, Kamalakkannan2 (AUTHOR), Jayaraman, Sasikala1 (AUTHOR)
Source: Imaging Science Journal. May2025, Vol. 73 Issue 3, p310-327. 18p.
Subjects: Principal components analysis, Image databases, Search algorithms, Detectors, Squirrels
Abstract: Near duplicate (ND) image detection is a significant issue in a modern online environment with a wide range of applications like the detection of copyright violations and saving of storage space. Several existing ND detection techniques are perhaps not suitable for online applications due to the large computational burden, and may not successfully detect NDs containing large smooth and plain regions. In addition, the K-means algorithm used in most of the existing methods yield sub-optimal quantization of visual words. This article employs a robust algorithm of Squirrel Search Optimization (SSO) for quantization, fast-hessian matrix-based detector (FHMBD) and FAST Corner Detector (FCD) for the detection of KPs at both plain and non-smooth regions of all images, SURF for computing descriptors and Principal Component Analysis (PCA) for dimensionality reduction. The results of the developed method presented on five image databases. The proposed method offered 99.9% accuracy, 98.67% sensitivity, and 99.91% specificity respectively. [ABSTRACT FROM AUTHOR]
Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Squirrel Search Optimization-based near-duplicate image detection.
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  Data: <searchLink fieldCode="AR" term="%22Sundaram%2C+Srinidhi%22">Sundaram, Srinidhi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> srinidhisristar@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Somasundaram%2C+Kamalakkannan%22">Somasundaram, Kamalakkannan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jayaraman%2C+Sasikala%22">Jayaraman, Sasikala</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Imaging+Science+Journal%22">Imaging Science Journal</searchLink>. May2025, Vol. 73 Issue 3, p310-327. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+databases%22">Image databases</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Squirrels%22">Squirrels</searchLink>
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  Label: Abstract
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  Data: Near duplicate (ND) image detection is a significant issue in a modern online environment with a wide range of applications like the detection of copyright violations and saving of storage space. Several existing ND detection techniques are perhaps not suitable for online applications due to the large computational burden, and may not successfully detect NDs containing large smooth and plain regions. In addition, the K-means algorithm used in most of the existing methods yield sub-optimal quantization of visual words. This article employs a robust algorithm of Squirrel Search Optimization (SSO) for quantization, fast-hessian matrix-based detector (FHMBD) and FAST Corner Detector (FCD) for the detection of KPs at both plain and non-smooth regions of all images, SURF for computing descriptors and Principal Component Analysis (PCA) for dimensionality reduction. The results of the developed method presented on five image databases. The proposed method offered 99.9% accuracy, 98.67% sensitivity, and 99.91% specificity respectively. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Imaging Science Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/13682199.2024.2390793
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 310
    Subjects:
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Image databases
        Type: general
      – SubjectFull: Search algorithms
        Type: general
      – SubjectFull: Detectors
        Type: general
      – SubjectFull: Squirrels
        Type: general
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      – TitleFull: Squirrel Search Optimization-based near-duplicate image detection.
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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