A Comparison between Neural Network and Support Vector Machine in Classifying Static and Real-Time Images

Authors

  • Ahmed Abdal Shafi Rasel  Department of Computer Science and Engineering, Stamford University Bangladesh
  • Aiasha Siddika  Department of Computer Science and Engineering, Stamford University Bangladesh
  • Md. Towhidul Islam Robin  Department of Computer Science and Engineering, Stamford University Bangladesh

DOI:

https://doi.org/10.32628/18410IJSRSET

Keywords:

Artificial Neural Network, Support Vector Machine, Image Classification

Abstract

The objective of this paper is to make an overall comparison between Neural Network (NN) and Support Vector Machine (SVM) in classifying static and real-time images. The dataset is composed of images from which the feature vector is extracted and given as training data for the classifiers. In this work, we are using Histogram of Oriented Gradients (HOG) as our feature vector. The experimental result shows SVM to be slightly overperforming Multi-Layer Perceptron (MLP) in detecting humans from static and real-time images.

References

  1. Cortes, C. & Vapnik, V. (1995). Support vector networks. Machine Learning, 20:3, 273-297.
  2. C. W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Networks, vol. 13, no. 2, pp. 415–425, 2002.
  3. Jacek M. Zurada, "Introduction to Artificial Neural Systems", 1994.
  4. K. Fukushima, "A heirarchical Neural Network Model for Pattern Recognition", pp. 263-270, 1990.
  5. B. Ojeda-Magaina, R. Ruelas, M. A. Corona-Nakamura and D. Andina, "An Improvement to the Possibilistic Fuzzy c-Means Clustering Algorithm," 2006 World Automation Congress, Budapest, 2006, pp. 1-8.
  6. "IEE Colloquium on 'Kalman Filters: Introduction, Applications and Future Developments' (Digest No.27)," IEE Colloquium on Kalman Filters: Introduction, Applications and Future Developments, London, UK, 1989
  7. M. I. Mohd Yusoff, I. Mohamed and M. R. A. Bakar, "Hidden Markov models: An insight," Proceedings of the 6th International Conference on Information Technology and Multimedia, Putrajaya, 2014, pp. 259-264.
  8. R. He, B. Hu, W. Zheng and X. Kong, "Robust Principal Component Analysis Based on Maximum Correntropy Criterion," in IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1485-1494, June 2011.
  9. Xingfu Zhang and Xiangmin Ren, "Two Dimensional Principal Component Analysis based Independent Component Analysis for face recognition," 2011 International Conference on Multimedia Technology, Hangzhou, 2011, pp. 934-936.
  10. Y. Zhou, S. Cao, D. Wen, H. Zhang and L. Zhao, "The study of face recognition based on hybrid principal components analysis and independent component analysis," 2011 International Conference on Electronics, Communications and Control (ICECC), Ningbo, 2011, pp. 2964-2966.
  11. Michal Kawulok, Jolanta Kawulok, and Jakub Nalepa. 2014, "Spatial-based skin detection using discriminative skin-presence features", Pattern Recogn. Lett. 41, C (May 2014), 3-13.
  12. Dalal, N.; Triggs, B., "Histograms of oriented gradients for human detection," Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on , vol.1, no., pp.886,893 vol. 1, 25-25 June 2005
  13. Qinyunlong and Sunguangling, "Handwritten character recognition using multi-resolution histograms of oriented gradients," 2008 11th IEEE International Conference on Communication Technology, Hangzhou, 2008, pp. 715-717
  14. M. R. Guedira, A. E. Qadi, M. R. Lrit and M. E. Hassouni, "A novel method for image categorization based on histogram oriented gradient and support vector machine," 2017 International Conference on Electrical and Information Technologies (ICEIT), Rabat, 2017, pp. 1-5.
  15. U. Budak and A. ?engUr, "Texture classification using scale invariant feature transform and Bag-of-Words," 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, 2015, pp. 152-155.
  16. W. Zhao, X. Chen, J. Cheng and L. Jiang, "An application of scale-invariant feature transform in iris recognition," 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS), Niigata, 2013, pp. 219-222.
  17. N. A. Arifin, B. Irawan and C. Setianingsih, "Traffic sign recognition application using speeded-up robust features (SURF) and support vector machine (SVM) based on android," 2017 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), Bandung, 2017, pp. 52-58.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-09-30

Issue

Volume 4, Issue 10, September-October-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ahmed Abdal Shafi Rasel, Aiasha Siddika, Md. Towhidul Islam Robin "A Comparison between Neural Network and Support Vector Machine in Classifying Static and Real-Time Images" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 10, pp.55-59, September-October-2018. Available at doi : https://doi.org/10.32628/18410IJSRSET