Near-duplicate detection for LCD screen acquired images using edge histogram descriptor

Mehta, Preeti; Tripathi, Rajiv Kumar

doi:10.1007/s11042-022-12637-1

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…The key issue here is that document files, voice data, and image data are easily tampered with and modified, which makes image copies known as Near-Duplicate (ND) difficult to detect [6] [7] .…”

Section: Introductionmentioning

confidence: 99%

Near-duplicate image fast detection based on annoy-SIFT algorithm

Dakui,

Minrui,

Kai

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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This thesis delves into the significance and challenges of Near-Duplicate Image Detection (NDD) in today's digital media landscape, where the varied manipulation of images complicates accurate identification. By designing and implementing a Near-Duplicate Image Detection method based on the Scale-Invariant Feature Transform (SIFT) algorithm, the paper aims to assist us in quickly and accurately identifying possible image duplication, cropping or other attacks. By detailing the key steps of the SIFT algorithm, including feature point extraction, outlier descriptor rejection strategy, and efficient matching using the Approximate Nearest Neighbor (Annoy) algorithm, the paper demonstrates the advantages of the method in improving detection efficiency and accuracy. In addition, the potential of image feature extraction in dealing with high-resolution image alignment is emphasized, as well as its importance for approximate duplicate detection and image copyright awareness protection. Through comparative experimental validation, the paper demonstrates that approximate image detection algorithms based on SIFT and Annoy algorithm have significant advantages in the field of image matching for large datasets, providing an effective tool for image detection and academic research.

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Section: Introductionmentioning

confidence: 99%