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.

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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