Object Detection Using Support Vector Machine and Convolutional Neural Network - A Survey

  • Authors

    • Rishi Khosla
    • Yashovardhan Singh
    • T Balachander
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12128
  • Convolutional Neural Network (CNN), Mobile Device, Scale-Invariant Feature Transform (SIFT), Image Detection, Image Classification, Support Vector Machine (SVM), Machine Learning.

  • Abstract

    Mobile Technologies have been in trend for quite some time and with the advances in machine learning, they have become more powerful. Computer Vision, Computational Analysis and Computer Graphics have changed over the course of time. In this Project, our aim is to figure out the domains in which Machine Learning can be applied to enhance the capabilities of a Mobile Device which would lead to a better and sustainable mobile user experience.  The models we would use are a convolutional neural network (CNN), support vector machine (SVM) and scale-invariant feature transform (SIFT). This project uses the real-time image from a mobile device and does the classification and detection with the help of Tensor Flow and provides the result with a confidence score.

     

  • References

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  • How to Cite

    Khosla, R., Singh, Y., & Balachander, T. (2018). Object Detection Using Support Vector Machine and Convolutional Neural Network - A Survey. International Journal of Engineering & Technology, 7(2.24), 428-430. https://doi.org/10.14419/ijet.v7i2.24.12128

    Received date: 2018-04-25

    Accepted date: 2018-04-25

    Published date: 2018-04-25