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

  • Authors

    • Simindokht Jahangard MSc, Department of Computer Engineering, Khorasgan Branch, Islamic Azad University, Isfahan, Iran
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.10575
  • image plagiarism, image retrieval, feature extracting, k-regular nearest neighbor graph, manifold-ranking.

  • 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.

     

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  • How to Cite

    Jahangard, S. (2018). Exploring Rotation and Scale Invariant Features in Image Pla- giarism Detection Using Manifold-Ranking Algorithm. International Journal of Engineering & Technology, 7(4), 2663-2671. https://doi.org/10.14419/ijet.v7i4.10575

    Received date: 2018-03-24

    Accepted date: 2018-06-07

    Published date: 2018-09-25