Jayshree Jain

Work place: Department of Computer Engineering, Pacific Academic Higher Education and Research University, Udaipur, India

E-mail: drjayshreejain@gmail.com


Research Interests: E-learning


Dr. Jayshree Jain is a professor in Pacific University. After graduating in IT, she did MTech, MBA and PhD. Her research area in Doctorate was & quot;Potential of e-Learning in Indian Education System & quot;. She is the life member of CSI, IIIE, ISTD and many other professional bodies. He has been the member of organizing committee for various national and international seminars / workshops. Dr. Jain has also completed SCJP Certification.

Author Articles
Enhancing Performance Evaluation for Video Plagiarism Detection Using Local Feature through SVM and KNN algorithm

By Ekta Thirani Jayshree Jain Vaibhav Narawade

DOI: https://doi.org/10.5815/ijigsp.2021.05.04, Pub. Date: 8 Oct. 2021

Nowadays in the digital world, there are lots of videos being uploaded to video, and social media sharing platforms are growing exponentially. About the Internet and multimedia technologies, illicitly copied content is a serious social problem. Since the internet is accessible to everyone, it is easy to download content and re-upload it. Copying videos from the internet can be considered plagiarism. In this paper, a method is proposed for feature extraction of video plagiarism detection. This framework is based on the local features to identify the videos frame by frame with the videos stored in the database. It becomes important to review the existing video plagiarism detection methods, compare them through appropriate performance metrics, list out their pros and cons and state the open challenges. First of all, it will pre-process the data with the help of SIFT and OCR Feature extraction. After that, the system applies the video retrieval and detection function using the two classifier algorithm the SVM, and the KNN. In the first stage, when the query is compared to all training data, KNN calculates the distances between the query and its neighbors and selects the K nearest neighbors. It is applied in the second stage to recognize the object using the SVM algorithm. Here we use the VSD dataset to predict the plagiarized videos. And the accuracy of these plagiarized videos after comparing them is 98%. 

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