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Logo and Brand Recognition from Imbalanced Dataset Using MiniGoogLeNet and MiniVGGNet Models

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Intelligent Information and Database Systems (ACIIDS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11431))

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Abstract

Deep learning model tends to promote models with deep structure. Despite its high accuracy, the model was not practical when high computing power was not available. Thus, deep model with not-so-deep structure or less number of model parameters is needed for low capacity computer. Logo and brand recognition task is an important and challenging problem in computer vision with wide potential applications. The inherent challenge to address this task is not only due to the presence of logo in various direction and clutters as well as imbalanced dataset but also because of high computing workload when deep learning models were adopted. This paper presents empirical results of logo recognition method using MiniVGGNet and MiniGoogleNet models combined with augmentation technique to increase variation and number of samples. The results show that the proposed model combined with augmentation technique increased accuracy of model accuracies and fasten training convergence of both models.

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References

  1. Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3(3), 210–229 (1959)

    Article  MathSciNet  Google Scholar 

  2. Hinton, G.E., Osindero, S., Teh, Y.-W.: Communicated by Yann Le Cun A fast learning algorithm for deep belief nets 500 units 500 units. Neural Comput. 18, 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  3. Bianco, S., Buzzelli, M., Mazzini, D., Schettini, R.: Deep learning for logo recognition. Neurocomputing 245, 23–30 (2017)

    Article  Google Scholar 

  4. Es-Sabry, M., El Akkad, N., Merras, M., Saaidi, A., Satori, K.: Grayscale image encryption using shift bits operations. In: 2018 International Conference on Intelligent System Computer Vision, ISCV 2018, no. Figure 3, May 2018

    Google Scholar 

  5. Allawi, S.T., Al-A’Meri, J.H.: Image encryption based on linear feedback shift register method. In: Al-Sadiq International Conference on multidisciplinary IT Communication Technical Science and Application, AIC-MITCSA 2016, pp. 16–19 (2016)

    Google Scholar 

  6. Wadi, S.M., Zainal, N., Abdulgader, A.: Grey scale image hiding method based on decomposition operation. In: Proceeding - 2013 IEEE Student Conference on Research and Development, SCOReD 2013, pp. 315–318, December 2015

    Google Scholar 

  7. Mohanty, M., Asghar, M.R., Russello, G.: 2DCrypt: image scaling and cropping in encrypted domains. IEEE Trans. Inf. Forensics Secur. 11(11), 2542–2555 (2016)

    Article  Google Scholar 

  8. Owen, C.B., Makedon, F.: High quality alias free image rotation. In: Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 115–119 (1997)

    Google Scholar 

  9. Barnett, S.M., Zambrini, R.: Resolution in rotation measurements. J. Mod. Opt. 53(5–6), 613–625 (2006)

    Article  Google Scholar 

  10. Su, H., Zhu, X., Gong, S.: Deep Learning Logo Detection with Data Expansion by Synthesising Context. CoRR, vol. abs/1612.0 (2016)

    Google Scholar 

  11. Kalantidis, Y., Pueyo, L.G., Trevisiol, M., van Zwol, R., Avrithis, Y.: Scalable triangulation-based logo recognition (Flickr 27). In: Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2011), pp. 20:1–20:7 (2011)

    Google Scholar 

  12. Romberg, S., Clara, S.: Scalable logo recognition in real-world Images categories and subject descriptors

    Google Scholar 

  13. Revaud, J., Schmid, C.: Correlation-Based Burstiness for Logo Retrieval Categories and Subject Descriptors: Keywords, pp. 965–968 (2012)

    Google Scholar 

  14. Revaud, J., et al.: DeepMatching: Hierarchical Deformable Dense Matching To cite this version: DeepMatching: Hierarchical Deformable Dense Matching (2015)

    Google Scholar 

  15. Romberg, S., Lienhart, R.: Bundle min-hashing for logo recognition. ACM (2013)

    Google Scholar 

  16. Boia, R., Bandrabur, A., Florea, C.: Local description using multi-scale complete rank transform for improved logo recognition, pp. 4–7 (2014)

    Google Scholar 

  17. Li, K., Chen, S., Su, S.: Logo detection with extendibility and discrimination (2013)

    Google Scholar 

  18. Zhang, Y., et al.: Deep learning for logo recognition. In: International Conference on Intelligent Systems Design and Applications, ISDA, vol. 245, no. 36, pp. 2051–2054 (2017)

    Google Scholar 

  19. Arivazhagan, N.: Logo Recognition. pp. 1–2

    Google Scholar 

  20. Su, H., Zhu, X., Gong, S.: Deep learning logo detection with data expansion by synthesising context. In: Proceedings of the 2017 IEEE Winter Conference Applications of Computer Vision (WACV 2017), pp. 530–539 (2017)

    Google Scholar 

  21. Pan, C., Yan, Z., Xu, X., Sun, M., Shao, J., Wu, D.: Learning architecture in video surveillance. pp. 123–126. IEEE (2013)

    Google Scholar 

  22. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1–9, 07–12 June 2015

    Google Scholar 

  23. Simonyan, K. Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1–14 (2015)

    Google Scholar 

  24. He, Z.: Deep residual learning for image recognition. arXiv.org e-Print Arch 7(3), 171–180 (2015)

    Google Scholar 

  25. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. ECCV 2016. LNCS, vol. 9908. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  26. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, January 2017, pp. 5987–5995 (2017)

    Google Scholar 

  27. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015)

    Google Scholar 

  28. Pyimagesearch: Deep Learning for computer Vision using Python. Book (2017)

    Google Scholar 

  29. Liu, W., et al.: SSD: single shot multibox detector. arXiv, pp. 1–15 (2016)

    Google Scholar 

  30. Girshick, R.: Fast R-CNN. arXiv.org e-Print Arch. (2015)

    Google Scholar 

  31. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Nips, pp. 1–10 (2015)

    Google Scholar 

  32. Impiombato, D., et al.: You only look once: unified, real-time object detection. Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip. 794, 185–192 (2015)

    Google Scholar 

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Acknowledgment

This research is partially supported by Binus IntelSys Research Interest Group.

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Authors and Affiliations

  1. Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia

    Sarwo, Yaya Heryadi, Widodo Budiharto & Edi Abdurachman

Authors
  1. Sarwo
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  2. Yaya Heryadi
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  3. Widodo Budiharto
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  4. Edi Abdurachman
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Corresponding author

Correspondence to Sarwo .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Bina Nusantara University, Jakarta, Indonesia

    Ford Lumban Gaol

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Sarwo, Heryadi, Y., Budiharto, W., Abdurachman, E. (2019). Logo and Brand Recognition from Imbalanced Dataset Using MiniGoogLeNet and MiniVGGNet Models. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-14799-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14798-3

  • Online ISBN: 978-3-030-14799-0

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