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.

  • 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

    1. [1] Yasutake, Taizo. "Graphical input controller and method with rear screen image detection." U.S. Patent 5,483,261, issued January 9, 1996.

      [2] Chum, Ondrej, James Philbin, and Andrew Zisserman. "Near Duplicate Image Detection: min-Hash and tf-idf Weighting." In BMVC, vol. 810, pp. 812-815. 2008.

      [3] Wang, Bin, Zhiwei Li, Mingjing Li, and Wei-Ying Ma. "Large-scale duplicate detection for web image search." In Multimedia and Expo, 2006 IEEE International Conference on, pp. 353-356. IEEE, 2006.

      [4] Hambly, N. C., M. J. Irwin, and H. T. MacGillivray. "The Super COSMOS Sky Survey—II. Image detection, parametrization, classification and photometry." Monthly Notices of the Royal Astronomical Society 326, no. 4 (2001): 1295-1314.

      [5] Sugiyama, Susumu, Ken Kawahata, Masakazu Yoneda, and Isemi Igarashi. "Tactile image detection using a 1k-element silicon pressure sensor array." Sensors and Actuators A: Physical 22, no. 1-3 (1990): 397-400.

      [6] Hsu, Yu-Feng, and Shih-Fu Chang. "Image splicing detection using camera response function consistency and automatic segmentation." In Multimedia and Expo, 2007 IEEE International Conference on, pp. 28-31. IEEE, 2007.

      [7] Miyasaka, Tsutomu, Koichi Koyama, and Isamu Itoh. "Quantum conversion and image detection by a bacteriorhodopsin-based artificial photoreceptor." Science255, no. 5042 (1992): 342-344.

      [8] S.V.Manikanthan and T.Padmapriya “Recent Trends In M2m Communications In 4g Networks And Evolution Towards 5gâ€, International Journal of Pure and Applied Mathematics, ISSN NO:1314-3395, Vol-115, Issue -8, Sep 2017.

      [9] T. Padmapriya and V.Saminadan, “Improving Performance of Downlink LTE-Advanced Networks Using Advanced Networks Using Advanced feedback Mechanisms and SINR Modelâ€, International Conference on Emerging Technology (ICET), vol.7, no.1, pp: 93, March 2014.

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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