Skip to main content
SpringerLink
Log in

Application of Broad Learning System for Image Classification Based on Deep Features

  • Conference paper
  • First Online:
Cognitive Systems and Signal Processing (ICCSIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1397))

Included in the following conference series:

Abstract

Due to large amount of image data, the accuracy and real-time performance of classification are difficult problems in image classification. On the one hand, the broad learning system (BLS) has achieved good results in the timeliness of classification. On the other hand, deep learning feature extraction effect is good, but its structure is complex and the training time is long. Therefore, based on the above advantages of both methods, this paper proposed BLS based on deep features for image classification. Firstly, a feature extractor is constructed based on the ResNet101 to obtain the deep features of the classification image. Then, the feature nodes and enhancement nodes of BLS are constructed based on the deep features. Through the experiment, our method has two performance on benchmark datasets: high classification accuracy, good real-time.

This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 51939001, Natural Foundation Guidance Plan Project of Liaoning 61976033, U1813203, 61803064, 61751202); (2019-ZD-0151, 2020-HYLH-26); Science & Technology Innovation Funds of Dalian (under Grant No. 2018J11CY022); The Liaoning Revitalization Talents Program (XLYC1807046, XLYC1908018); Fundamental Research Funds for the Central Universities (under Grant No. 3132019345).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1999). https://doi.org/10.1007/978-1-4757-3264-1

    Book  MATH  Google Scholar 

  2. Beucher, A., Moller, A.B., Greve, M.H.: Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Geoderma 320, 30–42 (2017)

    Google Scholar 

  3. Sonnenburg, S., Ratsch, G., Schafer, C., et al.: Large scale multiple kernel learning. J. Mach. Learn. Res. 7, 1531–1565 (2006)

    MathSciNet  MATH  Google Scholar 

  4. Wang, Y.F.: Image feature classification based on particle swarm optimization neural network. Metall. Min. Ind. 7(7), 328–334 (2015)

    Google Scholar 

  5. Zheng, Y.G., Wang, P., Ma, J., et al.: Remote sensing image classification based on BP neural network model. Trans. Nonferrous Metals Soc. China 15(1), 232–235 (2005)

    Google Scholar 

  6. Affonso, C., Sassi, R.J., Barreiros, R.M.: Biological image classification using rough-fuzzy artificial neural network. Expert Syst. Appl. 42(24), 9482–9488 (2015)

    Article  Google Scholar 

  7. Tzeng, Y.C., Chen, K.S.: Fuzzy neural network to SAR image classification. IEEE Trans. Geosci. Remote Sens. 36(1), 301–307 (1998)

    Article  Google Scholar 

  8. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504 (2006)

    Article  MathSciNet  Google Scholar 

  9. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

  10. Hinton, G.E., Osindero, S., The, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  11. Salakhutdinov, R., Hinton, G.: Deep Boltzmann machines. In: International Conference on Artificial Intelligence and Statistics, Florida, USA, pp. 448–455 (2009)

    Google Scholar 

  12. Babri, H.A., Tong, Y.: Deep feedforward networks: application to pattern recognition. In: International Conference on Neural Networks flCNN 1996, pp. 1422–1426. IEEE Press, Washington, USA (1996)

    Google Scholar 

  13. Lei, X.Y., Pan, H.G., Huang, X.D.: A dilated CNN model for image classification. IEEE Access 7, 124087–124095 (2019)

    Article  Google Scholar 

  14. Chaib, S., Yao, H.X., Gu, Y.F. et al.: Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models. In: Proceedings of SPIE - The International Society for Optical Engineering. Ninth International Conference on Digital Image Processing ICDIP 2017, vol. 10420 (2017)

    Google Scholar 

  15. Zhang, M.M., Li, W., Du, Q.: Diverse region-based CNN for hyperspectral image classification. IEEE Trans. Image Process. 27(6), 2623–2634 (2018)

    Article  MathSciNet  Google Scholar 

  16. Zhong, P., Gong, Z.Q., Schönlieb, C.: A diversified deep belief network for hyperspectral image classification. Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 41, 443–449 (2016). 23rd ISPRS Congress, Commission VII

    Article  Google Scholar 

  17. Lecun, G.E., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  18. Krizhevsky, A., Sutskever, I., Hinton, E.G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, LakeTahoe, Nevada, pp. 1097–1105. Curran Associates Inc, Red Hook (2012)

    Google Scholar 

  19. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Intenational Conference of Learning Representation, San Diego, CA (2015)

    Google Scholar 

  20. He, K.M., Zhang, X.Y., Ren, S.Q. et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, pp. 770–778. IEEE Computer Society, Los Alamitos (2016)

    Google Scholar 

  21. Szegedy, C., Liu, W., Jia, Y.Q., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 1–9. IEEE Press, Piscataway (2015)

    Google Scholar 

  22. Chen, P.C.L., Liu, Z.L.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29, 10–24 (2018)

    Article  MathSciNet  Google Scholar 

  23. Chen, P.C.L., Liu, Z., Feng, S.: Universal approximation capability of broad learning system and its structural variations. IEEE Trans. Neural Netw. Learn. Syst. 30, 1191–1204 (2018)

    Article  MathSciNet  Google Scholar 

  24. Chen, P.C.L., Liu, Z.: Broad learning system: a new learning paradigm and system without going deep. In: 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017, pp. 1271–1276 (2017)

    Google Scholar 

  25. Feng, S., Chen, P.C.L.: Fuzzy broad learning system: a novel neuro-fuzzy model for regression and classification. IEEE Trans. Cybern. 50, 414–424 (2018)

    Article  Google Scholar 

  26. Jin, J.W., Chen, P.C.L., Li, Y.T.: Regularized robust broad learning system for uncertain data modeling. Neurocomputing 322, 58–69 (2018)

    Article  Google Scholar 

  27. Jin, J.W., Liu, Z.L., Chen, P.C.L.: Discriminative graph regularized broad learning system for image recognition. Sci. China Inf. Sci. 61(11), 1–14 (2018)

    Article  Google Scholar 

  28. Liu, Z., Chen, P.C.L., Zhang, T., et al.: Multi-kernel broad learning systems based on random features: a novel expansion for nonlinear feature nodes. In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, IEEE International Conference on Systems, Man and Cybernetics, SMC 2019, pp. 193–197 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Navigation College, Dalian Maritime University, Dalian, 116026, China

    Dan Zhang, Yi Zuo, Philip C. L. Chen & Teishan Li

  2. Innovation and Entrepreneurship Education College, Dalian Minzu University, Dalian, 116600, China

    Dan Zhang & Chiye Wang

  3. Computer Science and Engineering College, South China University of Technology, Guangzhou, 510641, China

    Philip C. L. Chen

  4. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, 99999, China

    Philip C. L. Chen

  5. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Teishan Li

Authors
  1. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip C. L. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chiye Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Teishan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Zuo .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Tsinghua University, Beijing, China

    Huaping Liu

  3. Tsinghua University, Beijing, China

    Bin Fang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, D., Zuo, Y., Chen, P.C.L., Wang, C., Li, T. (2021). Application of Broad Learning System for Image Classification Based on Deep Features. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_44

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-2336-3_44

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2335-6

  • Online ISBN: 978-981-16-2336-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation