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Efficient Image Matching using Local Invariant Features for Copy Detection
Abstract
Retrieval based approach has recently emerged as an attractive option for image copy detection. The Content Based Copy Detection (CBCD) can be treated as a restricted case of near duplicate image detection. Near duplicate images can be: (i) perceptually identical images (e.g. allowing for change in color balance, change in brightness, compression artifacts, contrast adjustment, rotation, cropping, filtering, scaling etc.), (ii) images of the same 3D scene (from different viewpoints). As we are searching for copies which are altered versions of the original image, the images with slight viewpoint variations of the same scene should not be retrieved. In this chapter, we focus on image matching strategy based on local invariant features that will assist in the detection of forged (copy-paste forgery) images. So far, no specific robust homography estimation method exists for this application. The state of the art methodologies tend to generate many false positives. In this chapter, we have introduced a novel strategy for pattern matching of key point distributions for copy detection. Typical experiments conducted on real case images demonstrate the success in near duplicate image retrieval for the application of digital image forensics. Efficiency of the proposed method is corroborated by comparison, with contemporary methods.
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