Image classification using Deep learning

  • Authors

    • M Manoj krishna
    • M Neelima
    • M Harshali
    • M Venu Gopala Rao
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10892
  • AlexNet, Convolutional Neural Networks, Deep Learning, Image Classification, ImageNet, Machine Learning.

  • Abstract

    The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.

     

     

  • References

    1. [1] https://in.mathworks.com/matlabcentral/fileexchange/59133-neural-network-toolbox-tm--model-for-alexnet-network

      [2] H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009

      [3] Deep Learning with MATLAB – matlab expo2018

      [4] Introducing Deep Learning with the MATLAB – Deep Learning E-Book provided by the mathworks.

      [5] https://www.completegate.com/2017022864/blog/deep-machine-learning-images-lenet-alexnet-cnn/all-pages

      [6] Berg, J. Deng, and L. Fei-Fei. Large scale visual recognition challenge 2010. www.imagenet.org/challenges. 2010.

      [7] Fei-Fei Li, Justin Johnson and Serena Yueng, “Lecture 9: CNN Architectures†May 2017.

      [8] L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, 106(1):59–70, 2007.

      [9] J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1665–1672. IEEE, 2011.

      [10] https://in.mathworks.com/help/vision/examples/image-category-classification-using-deep-learning.html

      [11] Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks†May 2015.

      [12] A. Krizhevsky. Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto, 2009.

      [13] https://in.mathworks.com/help/nnet/deep-learning-imageclassification.html

      [14] KISHORE, P.V.V., KISHORE, S.R.C. and PRASAD, M.V.D., 2013. Conglomeration of hand shapes and texture information for recognizing gestures of indian sign language using feed forward neural networks. International Journal of Engineering and Technology, 5(5), pp. 3742-3756.

      [15] RAMKIRAN, D.S., MADHAV, B.T.P., PRASANTH, A.M., HARSHA, N.S., VARDHAN, V., AVINASH, K., CHAITANYA, M.N. and NAGASAI, U.S., 2015. Novel compact asymmetrical fractal aperture Notch band antenna. Leonardo Electronic Journal of Practices and Technologies, 14(27), pp. 1-12.

      [16] KARTHIK, G.V.S., FATHIMA, S.Y., RAHMAN, M.Z.U., AHAMED, S.R. and LAY-EKUAKILLE, A., 2013. Efficient signal conditioning techniques for brain activity in remote health monitoring network. IEEE Sensors Journal, 13(9), pp. 3273-3283.

      [17] KISHORE, P.V.V., PRASAD, M.V.D., PRASAD, C.R. and RAHUL, R., 2015. 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN, International Conference on Signal Processing and Communication Engineering Systems - Proceedings of SPACES 2015, in Association with IEEE 2015, pp. 34-38.

  • Downloads

  • How to Cite

    Manoj krishna, M., Neelima, M., Harshali, M., & Venu Gopala Rao, M. (2018). Image classification using Deep learning. International Journal of Engineering & Technology, 7(2.7), 614-617. https://doi.org/10.14419/ijet.v7i2.7.10892

    Received date: 2018-04-01

    Accepted date: 2018-04-01

    Published date: 2018-03-18