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Effective and Efficient Global Context Verification for Image Copy Detection
Subject area: Science,Engineering and Technology · Area of research: Image Processing
Abstract
To detect illegal copies of copyrighted images, recent copy detection methods mostly rely on the bag-of-visual- words (BOW) model, in which local features are quantized into visual words for image matching. However, both the limited discriminability of local features and the BOW quantization errors will lead to many false local matches, which make it hard to distinguish similar images from copies. Geometric consistency verification is a popular technology for reducing the false matches, but it neglects global context information of local features and thus cannot solve this problem well. To address this problem, this paper proposes a global context verification scheme to filter false matches for copy detection. More specifically, after obtaining initial scale invariant feature transform (SIFT) matches between images based on the BOW quantization, the overlapping region-based global context descriptor (OR-GCD) is proposed for the verification of these matches to filter false matches. The OR-GCD not only encodes relatively rich global context information of SIFT features but also has good robustness and efficiency.
Keywords
Image copy detection, near-duplicate detection, partial-duplicate detection, global context, overlapping region.
References
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[2] “Rani Susan Oommen Sree Buddha” “A Survey of CopyMove Forgery Detection Techniques for Digital Images” International journal of innovations in engineering and technology (IJIET).
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How to cite this paper
@article{1703698,
author = {Prof. Anil Kulkarni, Md Shujaath Khan, Md imran Ahmed, Bhagyashree Bapure, Zahoor Siddiqua},
title = {Effective and Efficient Global Context Verification for Image Copy Detection},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {1},
pages = {521-525},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703698.pdf},
abstract = {To detect illegal copies of copyrighted images, recent copy detection methods mostly rely on the bag-of-visual- words (BOW) model, in which local features are quantized into visual words for image matching. However, both the limited discriminability of local features and the BOW quantization errors will lead to many false local matches, which make it hard to distinguish similar images from copies. Geometric consistency verification is a popular technology for reducing the false matches, but it neglects global context information of local features and thus cannot solve this problem well. To address this problem, this paper proposes a global context verification scheme to filter false matches for copy detection. More specifically, after obtaining initial scale invariant feature transform (SIFT) matches between images based on the BOW quantization, the overlapping region-based global context descriptor (OR-GCD) is proposed for the verification of these matches to filter false matches. The OR-GCD not only encodes relatively rich global context information of SIFT features but also has good robustness and efficiency. },
keywords = {Image copy detection, near-duplicate detection, partial-duplicate detection, global context, overlapping region.},
month = {July},
}