Skip to main content

Advertisement

Springer Nature Link
Log in

Study on the influence of variable stride scale change on image recognition in CNN

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

After the research based on the progressing image classification recognition method of CNN, the paper aims at the problem that the size of feature size of output map of image with different complexity cannot be well solved by the constant value stride. We bring up the idea which based on the variable stride length for constraint parameters to selectively select the size of the stride. It is helpful to improve the efficiency of selective extraction and recognition of important features. Later studies have proved that the deficiency issue of complex image characteristic extraction due to the large stride size could be averted by adopting the variable stride length method based on constraint parameters. In the meantime, the method also avoids low recognition efficiency due to the image complexity is sparse and, also, the stride size of the image is too small. The theoretically calculated results are in good agreement with the experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Bengio Y (2009) Learning deep architectures for AI[J]. foundations and trends in. Mach Learn 2(1):1–127

    Article  MathSciNet  MATH  Google Scholar 

  2. Boureau YL, Ponce J, Lecun Y (2010) A theor-etical analysis of feature pooling in visual recognition [C]. Proc of International Conference on Machine Learning, p 111–118,

  3. Chan T-H, Jia K, Gao S et al (2015) PCANet:a simple deep learning baseline for image classification[J]. IEEE Trans Image Process 24(12):5017–5032

    Article  MathSciNet  MATH  Google Scholar 

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection[C]. Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on Piscataway, NJ: IEEE, p 886–893

  5. Fei-Fei L, Karpathy A, Johnson J (2016) CS231n:Convolutional neural networks for visual recognition. Stanford

  6. Goodfellow I, Bengio Y, Courville A (2016) Deeplearning. Book in preparation for MIT Press

  7. Han FY (2010) Analysis of modern multimedia technology features and key technologies. JCNU(NS) 29(3):129–131

    Google Scholar 

  8. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  9. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets [J]. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  10. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A con-volutional neural network for modeling sentences. arXiv preprint arXiv:1404.2188

  11. Koutník J, Greff K, Gomez F, et al (2014) A Cl-ockwork RNN [J]. Computer Science:1863–1871

  12. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images

  13. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks [C] . Proc of the Conf of Advances in Neural Information Processing Systems. Rostrevor, Ireland: Curran Associates, Inc, p 1097–1105

  14. Le QV, Ranzato M, Monga R, et al (2013) Building high-level features using large scale unsupervised learning [C]. Proc of the IEEE Int Conf on Acoustics, Speech and Signal Processing. Piscataway, NJ: IEEE, p 8595–8598

  15. Lecun Y, Boser B, Denker JS et al (2014) Backpropagation applied to handwritten zip code recognition [J]. Neural Comput 1(4):541–551

    Article  Google Scholar 

  16. Lee H, Grosse R, Ranganath R, et al (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations [C]. Proc of the 25th Annual Int Conf on Machine Learning. Piscataway, NJ: IEEE, p 609–616

  17. Dan Meng (2017) Research on image classification method based on deep learning. East China Normal University Press, p 5–15

  18. Qian Y (2012) An alternative algorithm for finding the maximum value of a function--golden section search method. JEIJP 28(12):140–142

    Google Scholar 

  19. Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge [J]. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  20. Wang B (2015) Research on image classification and image retrieval based on visual features and machine learning [D]. XiDian University, p 1–4

  21. Yang J, Yu K, Gong Y, et al (2009) Linear spatial pyramid matching using sparse coding for image classification [J]. 1794–1801

  22. Ye XY, Qin J (2006) The characteristics of shallow learning and deep learning are shown in the table. Educational technology guide. No.1, p 19–21

  23. Yu K (2013) Large-scale deep learning at Baidu [C]. Proc of ACM International Conferenceon Information & Knowledge Management. p 2211–2212

  24. Zhang C, Li X, Yan J, et al (2014) Sufficient statistics feature mapping over deep Boltzmann machine for detection[C]. International Conference on Pattern Recoginition (ICPR), p 827–832

  25. Zhang J, Wang H, Yang G, Xiao H (2018) Review of deep learning. ARC 35(7):1–2

    Google Scholar 

  26. Jun Zhu (2015) Image classfication based on deep learning models. Ningbo University. p 6–7

Download references

Acknowledgements

The Project was supported by the Natural Science Foundation of Liaoning Province (Grant No. 20170540131), Nature Science Foundation of Heilongjiang Province (Grant No.C201437), Natural Science Foundation of Heilongjiang Province (Grant No. QC2018082) and Basic Scientific Research Funds of Heilongjing Provincial Higher Education lnstitutions (Grant No. 2017-KYYWF-0140). And we wish to thank the anonymous reviewers who helped to improve the quality of the paper.

Author information

Authors and Affiliations

  1. Software Technology Institute, Dalian Jiaotong University, Dalian, 116021, China

    Chen Guo

  2. College of Computer Science, Harbin Normal University, Harbin, 150025, China

    Yue-lan Liu

  3. School of Information and Business Management, Dalian Neusoft University of Information, Dalian, 116021, China

    Xuan Jiao

Authors
  1. Chen Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yue-lan Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xuan Jiao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yue-lan Liu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, C., Liu, Yl. & Jiao, X. Study on the influence of variable stride scale change on image recognition in CNN. Multimed Tools Appl 78, 30027–30037 (2019). https://doi.org/10.1007/s11042-018-6861-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6861-0

Keywords