Exploring Rotation and Scale Invariant Features in Image Pla- giarism Detection Using Manifold-Ranking Algorithm

  • Abstract
  • Keywords
  • References
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  • Abstract

    Plagiarism, as a crucial offense especially in academia, not only is well-known problem in text but also is becoming widespread in image. In this work, the performance of manifold-ranking, known as robust method among semi-supervised methods, has been investigated by using twelve different features. As its high performance is attributed to the quality of constructed graph, we applied robust k-regular nearest neighbor (k-RNN) graph in the framework of manifold-ranking based retrieval. Among all tested feature point detectors and descriptors, Root-SIFT, the feature point ones, due to it is invariant to an array of image transforms, is the most reliable feature for calculating image similarity. The database consisting of images from scientific papers containing four popular benchmark test images served to test these methods.


  • Keywords

    image plagiarism, image retrieval, feature extracting, k-regular nearest neighbor graph, manifold-ranking.

  • References

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Article ID: 10575
DOI: 10.14419/ijet.v7i4.10575

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