International Journal of Soft Computing

Year: 2019
Volume: 14
Issue: 3
Page No. 53 - 60

Performance Evaluation of Support Vector Machines (SVM) and Convolution Neural Networks (CNN) for Video Tampering Classification

Authors : S.K. Komal, Puneeth Chandrashekar, B.S. Rekha and G.N. Srinivasan

References

Ashwin, T.S., S. Saran and G.R.M. Reddy, 2016. Video affective content analysis based on multimodal features using a novel hybrid SVM-RBM classifier. Proceedings of the 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON’16), December 9-11, 2016, IEEE, Varanasi, India, pp: 416-421.

Burney, A. and T.Q. Syed, 2016. Crowd video classification using convolutional neural networks. Proceedings of the 2016 International Conference on Frontiers of Information Technology (FIT’16), December 19-21, 2016, IEEE, Islamabad, Pakistan, pp: 247-251.

Karpathy, A., G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, 2014. Large-scale video classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’2014), June 2014, IEEE, New York, USA., pp: 1725-1732.

Wang, L. and D. Sng, 2015. Deep learning algorithms with applications to video analytics for a smart city: A survey. J. IEEE., Vol. 1,

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