Advertisement

Using Convolutional Neural Networks to Recognition of Dolphin Images

  • Yadira Quiñonez
  • Oscar Zatarain
  • Carmen Lizarraga
  • Juan Peraza
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)

Abstract

Classification of specific objects through Convolutional Neural Networks (CNN) has become an interesting research line in the area from information processing and machine learning, main idea is training a image dataset to perform the classifying a given pattern. In this work, a new dataset with 2504 images was introduced, the method used to train the networks was transfer learning to recognition of dolphin images. For this purpose, two models were used: Inception V3 and Inception ResNet V2 to train on TensorFlow platform with different images, corresponding to the four main classes: dolphin, dolphin_pod, open_sea, and seabirds. The paper ends with a critical discussion of the experimental results.

Keywords

Convolutional neural networks Machine learning Deep learning TensorFlow Inception V3 Inception ResNet V2 

Notes

Acknowledgments

The authors would like to thank Universidad Autónoma de Sinaloa for supporting and financing this research project.

References

  1. 1.
    Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017)CrossRefGoogle Scholar
  2. 2.
    Huang, F., Sun, T., Bu, F.: Generation of person-specific 3D model based on single photograph. In: 2nd IEEE International Conference on Computer and Communications, pp. 704–707. IEEE Press (2016)Google Scholar
  3. 3.
    Choi, W., Chao, Y.W., Pantofaru, C., Savarese, S.: Discovering groups of people in images. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014. Lecture Notes in Computer Science, vol. 8692, pp. 417–433. Springer, Cham (2014)Google Scholar
  4. 4.
    Ouarda, W., Trichili, H., Alimi, A.M., Solaiman, B.: Face recognition based on geometric features using support vector machines. In: 6th International Conference of Soft Computing an Pattern Recognition. pp. 89–95 (2014)Google Scholar
  5. 5.
    Chen, Q., Kotani, K., Lee, F.: Face recognition using multiple histogram features in spatial and frequency domains. In: 12th International Conference on Signal-Image Technology Internet-Based Systems, pp. 204–208. IEEE Press (2016)Google Scholar
  6. 6.
    Atallah, R.R., Kamsin, A., Ismail, M.A., Abdelrahman, S.A., Zerdoumi, S.: Face recognition and age estimation implications of changes in facial features: a critical review study. IEEE Access 6, 28290–28304 (2018)CrossRefGoogle Scholar
  7. 7.
    Bradbury, G., Mitchell, K., Weyrich, T.: Multi-spectral material classification in landscape scenes using commodity hardware. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, vol. 8048, pp. 209–216. Springer, Berlin (2013)CrossRefGoogle Scholar
  8. 8.
    Lu, W.S.: Handwritten digits recognition using PCA of histogram of oriented gradient. In: IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp. 1–5. IEEE Press (2017)Google Scholar
  9. 9.
    Larasati, R., KeungLam, H.: Handwritten digits recognition using ensemble neural networks and ensemble decision tree. In: International Conference on Smart Cities, Automation Intelligent Computing Systems, pp. 99–104. IEEE Press (2017)Google Scholar
  10. 10.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  11. 11.
    Awad, M., Khanna, R.: Deep learning. In: Efficient Learning Machines, pp 167–184. Apress, Berkeley (2015)Google Scholar
  12. 12.
    Wu, Q., Liu, Y., Li, Q., Jin, S., Li, F.: The application of deep learning in computer vision. In: Chinese Automation Congress, pp. 6522–6527. IEEE Press (2017)Google Scholar
  13. 13.
    Goswami, T.: Impact of deep learning in image processing and computer vision. In: Anguera, J., Satapathy, S., Bhateja, V., Sunitha, K. (eds.) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol. 471, pp. 475–485. Springer, Singapore (2018)Google Scholar
  14. 14.
    Sustika, R., Yuliani, A.R., Zaenudin, E., Pardede, H.F.: On comparison of deep learning architectures for distant speech recognition. In: 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, pp. 17–21. IEEE Press (2017)Google Scholar
  15. 15.
    Miyajima, R.: Deep learning triggers a new era in industrial robotics. MultiMedia 24(4), 91–96 (2017)CrossRefGoogle Scholar
  16. 16.
    Heck, L., Huang, H.: Deep learning of knowledge graph embeddings for semantic parsing of Twitter dialogs. In: Global Conference on Signal and Information Processing, pp. 597–601. IEEE Press (2014)Google Scholar
  17. 17.
    Moriya, S., Shibata, C.: Transfer learning method for very deep CNN for text classification and methods for its evaluation. In: 42nd Annual Computer Software and Applications Conference, pp. 153–158 (2018)Google Scholar
  18. 18.
    Alshahrani, S., Kapetanios, E.: Are deep learning approaches suitable for natural language processing? In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) Natural Language Processing and Information Systems. Lecture Notes in Computer Science, vol. 9612, pp. 343–349. Springer, Cham (2016)Google Scholar
  19. 19.
    He, X., Deng, L.: Deep learning in natural language generation from images. In: Deng, L., Liu, Y. (eds.) Deep Learning in Natural Language Processing, pp. 289–307. Springer, Singapore (2018)CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Guresen, E., Kayakutlu, G.: Definition of artificial neural networks with comparison to other networks. Procedia Comput. Sci. 3, 426–433 (2011)CrossRefGoogle Scholar
  22. 22.
    Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T.: Recent advances in convolutional neural networks. J. Pattern Recognit. 77, 354–377 (2018)CrossRefGoogle Scholar
  23. 23.
    Cheng, J., Wang, P., Li, G., Hu, Q., Lu, H.: Recent advances in efficient computation of deep convolutional neural networks. Front. Inf. Technol. Electron. Eng. 19(1), 64–77 (2018)CrossRefGoogle Scholar
  24. 24.
    Habibi, A.H., Jahani, H.E.: Convolutional neural networks. In: Guide to Convolutional Neural Networks, pp. 85–130. Springer, Cham (2017)Google Scholar
  25. 25.
    Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Into Imaging 1–19 (2018)Google Scholar
  26. 26.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  27. 27.
    Weiss, K., Khoshgoftaar, T.M., Wang, D.J.: A survey on transfer learning. J. Big Data 3(9), 1–40 (2016)Google Scholar
  28. 28.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint: arXiv:1512.00567 (2015)
  29. 29.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint: arXiv:1512.03385 (2015)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yadira Quiñonez
    • 1
  • Oscar Zatarain
    • 1
  • Carmen Lizarraga
    • 1
  • Juan Peraza
    • 1
  1. 1.Universidad Autónoma de Sinaloa, Facultad de Informática MazatlánMazatlánMexico

Personalised recommendations