Journal of Soft Computing Paradigm
IRO Journals
Volume 4 — Issue 2 — June 2022

Using SVM and KNN to Evaluate Performance Based on Video Plagiarism Detectors and Descriptors for Global Features

https://doi.org/10.36548/jscp.2022.2.004
Ekta Thirani
Computer Engineering, Pacific Academy of Higher Education and Research University, Udaipur
Jayshree Jain
Department of Computer Engineering, Pacific Academy of Higher Education and Research University, Udaipur
Vaibhav Narawade
Department of Computer Engineering, Ramrao Adik Institute of Technology, Nerul
PDF
PDF

How to Cite

Thirani, Ekta, Jayshree Jain, and Vaibhav Narawade. 2022. “Using SVM and KNN to Evaluate Performance Based on Video Plagiarism Detectors and Descriptors for Global Features”. Journal of Soft Computing Paradigm 4 (2): 82-100. https://doi.org/10.36548/jscp.2022.2.004.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Feature extraction
— video copy detection
— copyright protection
— video security
— SVM
— KNN
— and video plagiarism detection
Published: 25-07-2022

Abstract

The detection of video piracy has improved and emerged as a popular issue in the field of digital video copyright protection because a sequence of videos often comprises a huge amount of data. The major difficulty in achieving efficient and simple video copy detection is to identify compressed and exclusionary video characteristics. To do this, we describe a video copy detection strategy that created the properties for a spatial-temporal domain. The first step is to separate each video sequence into the individual video frame, and then extract the boundaries of each video frame by using PCA SIFT and Hessian- Laplace. Next, for each video frame, we have to implement SVM and KNN features in the spatial and temporal domains to measure their performance matrices in the feature extraction. Finally, the global features found in the Video copy detection are accomplished uniquely and efficiently. Experiments arranged a commonly used VCDB 2014 video dataset, showing that result. The proposed approach is based on various copy detection algorithms and shows various features in terms of both accuracy and efficiency.

References

  1. Janwe, Nitin J., and Kishor K. Bhoyar. "Neural network-based multi-label semantic video concept detection using the novel mixed-hybrid-fusion approach." Proceedings of the 2nd International Conference on Communication and Information Processing. ACM, 2016.
  2. M. Yeh, K. T. Cheng, “Video copy detection by fast sequence matching”, in ACM Proc. of Int.Conf. on Image, Video Retrieval, 2009.
  3. Ullah, F., Wang, J., Farhan, M., Jabbar, S., Wu, Z., & Khalid, S. (2020). Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology. Multimedia tools and applications, 79(13), 8581-8598.
  4. Hampapur, A., Hyun, K., & Bolle, R. M. (2017, December). Comparison of sequence matching techniques for video copy detection. In Storage and Retrieval for Media Databases 2002 (Vol. 4676, pp. 194-201). International Society for Optics and Photonics.
  5. Jiang, Q. Y., He, Y., Li, G., Lin, J., Li, L., & Li, W. J. (2019). SVD: A large-scale short video dataset for near-duplicate video retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5281-5289).
  6. Wang, Y., Hou, Z., Leman, K., Pham, N. T., Chua, T., & Chang, R. (2011, January). Combination of local and global features for near-duplicate detection. In International Conference on Multimedia Modeling (pp. 328-338). Springer, Berlin, Heidelberg.
  7. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020, June). CNN-SVM learning approach is based on human activity recognition. In International Conference on Image and Signal Processing (pp. 271-281). Springer, Cham.
  8. Muralidharan, R., & Chandrasekar, C. (2012, March). Combining local and global features for object recognition using SVM-KNN. In International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) (pp. 1-7). IEEE.
  9. Jiang, Y. G., Jiang, Y., & Wang, J. (2014, September). VCDB: a large-scale database for partial copy detection in videos. In European conference on computer vision (pp. 357-371). Springer, Cham.
  10. Liu, H., Fan, Z., Chen, Q., & Zhang, X. (2022). Enhancing face detection in video sequences by video segmentation preprocessing. Applied Intelligence, 1-11.
  11. Karthika, P., & VidhyaSaraswathi, P. (2018, May). Digital video copy detection using steganography frame-based fusion techniques. In International Conference on ISMAC in Computational Vision and bio-engineering (pp. 61-68). Springer, Cham.
  12. Zhou, Z., Yang, C. N., Chen, B., Sun, X., Liu, Q., & QM, J. (2016). Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Transactions on Information and systems, 99(6), 1531-1540.
  13. Yeh, M. C., & Cheng, K. T. (2009, July). Video copy detection by fast sequence matching. In Proceedings of the ACM International Conference on Image and Video Retrieval (pp. 1-7).
  14. Shinde, S. R., & Chiddarwar, G. G. (2015, January). Recent advances in content-based video copy detection. In 2015 International conference on pervasive computing (ICPC) (pp. 1-6). IEEE.
  15. Lian, S., Nikolaidis, N., & Sencar, H. T. (2010). Content-based video copy detection–a survey. Intelligent Multimedia Analysis for Security Applications, 253-273.
  16. Rajesekaran and Vijayalakshmi Pai. (2011). Object recognition using SVM-KNN based on geometric moment invariant. International Journal of Computer Trends and Technology, 1(1), 215-220.