Image Classification using CNN and Machine Learning

Authors

  • G. Keerthi Devipriya  B.Tech, Department of Computer Science and Engineering, VVIT, Guntur , Andhra Pradesh, India
  • E. Chandana  B.Tech, Department of Computer Science and Engineering, VVIT, Guntur , Andhra Pradesh, India
  • B. Prathyusha  B.Tech, Department of Computer Science and Engineering, VVIT, Guntur , Andhra Pradesh, India
  • T. Seshu Chakravarthy  Assistant Professor, Department of Computer Science and Engineering, VVIT, Guntur , Andhra Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT195298

Keywords:

Classifier, Feature Extraction, BoF, Supervised Learning

Abstract

Here by in this paper we are interested for classification of Images and Recognition. We expose the performance of training models by using a classifier algorithm and an API that contains set of images where we need to compare the uploaded image with the set of images available in the data set that we have taken. After identifying its respective category the image need to be placed in it. In order to classify images we are using a machine learning algorithm that comparing and placing the images.

References

  1. A.Chergui, A.Bekkhoucha, W.Sabbar, “Video scene segmentation using the shot transition detection by local characterization of the points of interest”. SETIT’2012,Tunisie DOI:10.1109/SETIT.2012.6481949, 2012.
  2. M. S.Nixon, A. S.Aguado, “Feature Extraction and Image Processing”. Elsevier Ltd, ISBN: 978-0-12372-538-7, 2008.
  3. C.Domeniconi, D.Gunopulos, J.Peng, “Large margin nearest neighbor classifiers”. IEEE Transactions on Neural Networks, 16(4):899–909, 2005.
  4. D. G.Lowe, “Distinctive image features from scale invariant keypoints”. IJCV, 60(2):91–110, 2004.
  5. Tianmei Guo, Jiwen Dong, Henjian Li, Yunxing Gao, "Simple Convolution Neural Network on Image Classification".
  6. J. H.Friedman, “Greedy Function Approximation: A Gradient Boosting Machine”, 1999.
  7. L.Rokach, O.Maimon, “Data mining with decision trees: theory and applications”. World Scientific Pub Co Inc. ISBN 978-9812771711, 2008.
  8. Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2013, 35(8):1798-1828.
  9. Graham, B., Fractional Max-Pooling. 2014. Ar Xiv e-prints, December 2014a.
  10. Deeply supervised nets. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2015, San Diego, California, USA, May9-12, 2015.
  11. Hecht-Nielsen R. Theory of the backpropagation neural network[M]// Neural networks for perception (Vol. 2). Harcourt Brace & Co. 1992:593-605 vol.1.
  12. Y. Boureau, J. Ponce, and Y. Le Cun, “A theoretical analysis of feature pooling in visual recognition,” in ICML, 2010, pp. 111–118.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
G. Keerthi Devipriya, E. Chandana, B. Prathyusha, T. Seshu Chakravarthy, " Image Classification using CNN and Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.575-580, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195298