Advertisement

A Model for Identifying Historical Landmarks of Bangladesh from Image Content Using a Depth-Wise Convolutional Neural Network

  • Afsana Ahsan Jeny
  • Masum Shah Junayed
  • Syeda Tanjila AtikEmail author
  • Sazzad Mahamd
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

At present, tourism is considered to be one of the key factors shaping the development of a country’s economy. Most of the tourists tend to explore places that they find fascinating after watching pictures of that places over Internet. Anyone can know about a famous place by simply typing the name of that place in an internet browser. But problem arises when he/she comes across the image of a beautiful landmark which is anonymous as most of the time web images do not convey any text caption. Most of models provided for image identification so far exhibit much complex structure and increased time complexity. In this paper, we have proposed a CNN model based on MobileNet and TensorFlow for detecting some historical landmarks of Bangladesh from their image. We have examined 750 images from five different places and comparing other state-of-art models, our model holds relatively simpler structure and has achieved a significantly higher average accuracy of 99.2%. This model can be further enhanced to facilitate image classification in other related areas.

Keywords

Convolutional neural networks TensorFlow MobileNet Historical place detection Image processing 

References

  1. 1.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions, arXiv:1409.4842v1 [cs.CV], 17 September 2014
  2. 2.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv:1602.07261 [cs.CV]
  3. 3.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J.: Rethinking the Inception Architecture for Computer Vision,arXiv:1512.00567v1 [cs.CV], 2 December 2015
  4. 4.
    Amin, K., Hussain, M., Ujang, N.: Visitors’ Identification of Landmarks in the Historic District of Banda Hilir, Melaka, Malaysia. In: AMER International Conference on Quality of Life, AicQoL2014KotaKinabaluGoogle Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Deep Learning with Tensorflow. http://cvml.ist.ac.at/courses/DLWT_W17/
  6. 6.
    Beynon, M.J., Jones, C., Munday, M., Roche, N.: Investigating value added from heritage assets: an analysis of landmark historical sites in Wales. Int. J. Tour. Res. 20(6), 756–767 (2018)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (NIPS 2012)Google Scholar
  8. 8.
    Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv:1409.1556v3 [cs.CV] 18 November 2014
  9. 9.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla,A., Bernstein, M., Berg, A.C., Li, F.-F.: ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575v3 [cs.CV], 30 January 2015
  10. 10.
    Howar, A.G., Zhu,M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861v1 [cs.CV], 17 April 2017
  11. 11.
    Abadi, M., Agarwal, A., Barham, P., Brevdo, E.: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (Preliminary White Paper, November 9, 2015), arXiv:1603.04467v2 [cs.DC], 16 March 2016
  12. 12.
    Kaiser, L., Gomez, A.N., Chollet, F.: Depthwise Separable Convolutions for Neural Machine Translation. arXiv:1706.03059v2 [cs.CL], 16 Jun 2017
  13. 13.
    https://www.docker.com/ Docker Simplifies the Developer Experience
  14. 14.
    The details of Confusion matrix. https://en.wikipedia.org/wiki/Confusion_matrix
  15. 15.
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385v1 [cs.CV], 10 December 2015
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition arXiv:1409.1556v6 [cs.CV], 10 April 2015
  18. 18.
    Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks, arXiv:1608.06993v5 [cs.CV], 28 January 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Afsana Ahsan Jeny
    • 1
  • Masum Shah Junayed
    • 1
  • Syeda Tanjila Atik
    • 1
    Email author
  • Sazzad Mahamd
    • 1
  1. 1.Daffodil International UniversityDhakaBangladesh

Personalised recommendations