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

Authors(3) :-Ahmed Abdal Shafi Rasel, Aiasha Siddika, Md. Towhidul Islam Robin

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.

Authors and Affiliations

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

Artificial Neural Network, Support Vector Machine, Image Classification

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Publication Details

Published in : Volume 4 | Issue 10 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 55-59
Manuscript Number : IJSRSET1841022
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

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
Journal URL : http://ijsrset.com/IJSRSET1841022

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