Punyanuch Borwarnginn, Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Kittikhun Thongkanchorn. Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning. International Journal of Automation and Computing, vol. 18, no. 1, pp.45-54, 2021. https://doi.org/10.1007/s11633-020-1261-0
Citation: Punyanuch Borwarnginn, Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Kittikhun Thongkanchorn.

Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning

. International Journal of Automation and Computing, vol. 18, no. 1, pp.45-54, 2021. https://doi.org/10.1007/s11633-020-1261-0

Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning

doi: 10.1007/s11633-020-1261-0
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  • Author Bio:

    Punyanuch Borwarnginn received the B. Sc. degree in information and communication technology from Mahidol University, Thailand in 2009, and the M. Sc. degree in informatics from the University of Edinburgh, UK in 2011. She is currently a Ph. D. degree candidate in computer science from Faculty of Information and Communication Technology, Mahidol University, Thailand.Her research interests include image processing, biometrics, computer vision, pattern recognition and machine learning. E-mail: punyanuch.bor@mahidol.edu ORCID iD: 0000-0002-6309-5022

    Worapan Kusakunniran received the B.Eng. degree in computer engineering from the University of New South Wales (UNSW), Australia in 2008, and the Ph. D. degree in computer science and engineering from UNSW, in cooperation with the Neville Roach Laboratory, National ICT Australia, Australia in 2013. He is currently a lecturer with Faculty of Information and Communication Technology, Mahidol University, Thailand. He is the author of several papers in top international conferences and journals. Dr. Kusakunniran served as a program committee member for many international conferences and workshops. Also, he has served as a reviewer for several international conferences and journals, such as International Conference on Pattern Recognition, IEEE International Conference on Image Processing, IEEE International Conference on Advanced Video and Signal Based Surveillance, Pattern Recognition, IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. His research interests include biometrics, pattern recognition, medical image processing, computer vision, multimedia, and machine learning.E-mail: worapan.kun@mahidol.edu (Corresponding author) ORCID iD: 0000-0002-2896-611X

    Sarattha Karnjanapreechakorn received the B. Sc. degree in electrical-mechanical manufacturing engineering from Kasertsart University, Thailand in 2015, and the M. Sc. degree in game technology and gamification from Mahidol University, Thailand in 2017. He is currently a Ph. D. degree candidate in computer science of Faculty of Information and Communication Technology, Mahidol University, Thailand. His research interests include image processing, biometrics, computer vision, pattern recognition and machine learning. E-mail: sarattha.kar@student.mahidol.ac.th

    Kittikhun Thongkanchorn received the B. Sc. degree in information and communication technology from University of Mahidol, Thailand in 2007, and the M. Sc degree in computer science from Faculty of ICT, Mahidol University, Thailand in 2012. He is currently a computer scientist, senior professional level with Faculty of ICT, Mahidol University, Thailand.His research interests include computer system and network, elastic computing and distributed system, computer security and policy, image processing and machine learning. E-mail: kittikhun.tho@mahidol.edu

  • Received Date: 2020-06-04
  • Accepted Date: 2020-09-30
  • Publish Online: 2020-11-13
  • Publish Date: 2021-02-18
  • Dog breed identification is essential for many reasons, particularly for understanding individual breeds′ conditions, health concerns, interaction behavior, and natural instinct. This paper presents a solution for identifying dog breeds using their images of their faces. The proposed method applies a deep learning based approach in order to recognize their breeds. The method begins with a transfer learning by retraining existing pre-trained convolutional neural networks (CNNs) on the public dog breed dataset. Then, the image augmentation with various settings is also applied on the training dataset, in order to improve the classification performance. The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons. The proposed model achieves a promising accuracy of 89.92% on the published dataset with 133 dog breeds.

     

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  • [1]
    S. G. Tong, Y. Y. Huang, Z. M. Tong. A robust face recognition method combining LBP with multi-mirror symmetry for images with various face interferences. International Journal of Automation and Computing, vol. 16, no. 5, pp. 671–682, 2019. DOI: 10.1007/s11633-018-1153-8.
    [2]
    F. K. Zaman, A. A. Shafie, Y. M. Mustafah. Robust face recognition against expressions and partial occlusions. International Journal of Automation and Computing, vol. 13, no. 4, pp. 319–337, 2016. DOI: 10.1007/s11633-016-0974-6.
    [3]
    J. R. Xue, J. W. Fang, P. Zhang. A survey of scene understanding by event reasoning in autonomous driving. International Journal of Automation and Computing, vol. 15, no. 3, pp. 249–266, 2018. DOI: 10.1007/s11633-018-1126-y.
    [4]
    M. Chanvichitkul, P. Kumhom, K. Chamnongthai. Face recognition based dog breed classification using coarse-to-fine concept and PCA. In Proceedings of Asia-Pacific Conference on Communications, IEEE, Bangkok, Thailand, pp. 25–29, 2007.
    [5]
    P. Prasong, K. Chamnongthai. Face-recognition-based dog-breed classification using size and position of each local part, and PCA. In Proceedings of the 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, IEEE, Phetchaburi, Thailand, 2012.
    [6]
    N. Dalal, B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Diego, USA, pp. 886–893, 2005.
    [7]
    D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. DOI: 10.1023/B:VISI.0000029664.99615.94.
    [8]
    O. M. Parkhi, A. Vedaldi, A. Zisserman, C. V. Jawahar. Cats and dogs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Providence, USA, pp. 3498–3505, 2012.
    [9]
    J. X. Liu, A. Kanazawa, D. Jacobs, P. Belhumeur. Dog breed classification using part localization. In Proceedings of the 12th European Conference on Computer Vision, Springer, Florence, Italy, pp. 172–185, 2002.
    [10]
    K. Lai, X. Y. Tu, S. Yanushkevich. Dog identification using soft biometrics and neural networks. In Proceedings of International Joint Conference on Neural Networks, IEEE, Budapest, Hungary, pp. 1–8, 2019.
    [11]
    X. Y. Tu, K. Lai, S. Yanushkevich. Transfer learning on convolutional neural networks for dog identification. In Proceedings of the 9th IEEE International Conference on Software Engineering and Service Science, IEEE, Beijing, China, pp. 357–360, 2018.
    [12]
    B. Zhao, J. S. Feng, X. Wu, S. C. Yan. A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, vol. 14, no. 2, pp. 119–135, 2017. DOI: 10.1007/s11633-017-1053-3.
    [13]
    O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. A. Ma, Z. H. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, F. F. Li. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015. DOI: 10.1007/s11263-015-0816-y.
    [14]
    C. Szegedy, V. Vanhoucke, S Ioffe, J. Shlens, Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 2818–2826, 2016. DOI: 10.1109/CVPR.2016.308.
    [15]
    C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going deeper with convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, MA, USA, pp. 1–9, 2015.
    [16]
    M. Sandler, A. Howard, M. L. Zhu, A. Zhmoginov, L. C. Chen. Mobilenetv2: inverted residuals and linear bottlenecks. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 4510–4520, 2018.
    [17]
    B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le. Learning transferable architectures for scalable image recognition. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 8697–8710, 2018. DOI: 10.1109/CVPR.2018.00907.
    [18]
    J. Yosinski, J. Clune, Y. Bengio, H. Lipson. How transferable are features in deep neural networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems, MIT Press, Montreal, Canada, pp. 3320–3328, 2014.
    [19]
    L. Shao, F. Zhu, X. L. Li. Transfer learning for visual categorization: a survey. IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 5, pp. 1019–1034, 2015. DOI: 10.1109/TNNLS.2014.2330900.
    [20]
    J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, F. F. Li. ImageNet: a large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Miami, USA, pp. 248–255, 2009.
    [21]
    T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick. Microsoft coco: common objects in context. In Proceedings of the 13th European Conference on Computer Vision, Springer, Zurich, Switzerland, pp. 740–755, 2014.
    [22]
    N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
    [23]
    K. Weiss, T. M. Khoshgoftaar, D. D. Wang. A survey of transfer learning. Journal of Big Data, vol. 3, no. 1, Article number 9, 2016. DOI: 10.1186/s40537-016-0043-6.
    [24]
    A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, S. Savarese. Taskonomy: disentangling task transfer learning. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 3712–3722, 2018.
    [25]
    S. Ioffe, C. Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, pp. 448–456, 2015.
    [26]
    C. Shorten, T. M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, vol. 6, no. 1, Article number 60, 2019.
    [27]
    L. Perez, J. Wang. The effectiveness of data augmentation in image classification using deep learning. [online], Available: https://arxiv.orglabs/1712.04621, 2017.
    [28]
    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 2672–2680, 2014.
    [29]
    A. Khosla, N. Jayadevaprakash, B. P. Yao, F. F. Li. Novel dataset for fine-grained image categorization: Stanford dogs. In Proceedings of the 1st Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Colorado Springs, USA, 2011.
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