Skip to main content
SpringerLink
Log in

Object Recognition Based on Dynamic Random Forests and SURF Descriptor

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Visual object recognition is an extremely difficult computational problem. It is still a challenging task for computer vision systems related especially to the high variability of the image of objects that may vary somewhat in different viewpoints, in many different sizes and scales or even when they are translated or rotated. In this study, we investigate the combination of a new dynamic random forests and SURF descriptor for object recognition. We have carried out experiments on two benchmark object recognition datasets: CIFAR-10 and STL-10. The experimental results show the superior ability of our proposed approach, compared to the standard RF in terms of recognition rate and execution time.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Bai, S.: Growing random forest on deep convolutional neural networks for scene categorization. Expert Syst. Appl. 71, 279–287 (2017)

    Article  Google Scholar 

  2. Zhao, W., Xiong, L., Ding, H.: Automatic recognition of loess landforms using Random Forest method. J. Mt. Sci. 14(5), 885–897 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  4. Gecer, B., Azzopardi, G., Petkov, N.: Color-blob-based COSFIRE filters for object recognition. Image Vis. Comput. 57, 165–174 (2017)

    Article  Google Scholar 

  5. Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3367–3375 (2015)

    Google Scholar 

  6. Dicarlo, J., Cox, D.: Untangling invariant object recognition. Trends Cogn. Sci. 11(8), 333–341 (2007)

    Article  Google Scholar 

  7. Zhang, L., He, Z., Liu, Y.: Deep object recognition across domains based on adaptive extreme learning machine. Neurocomputing 239, 194–203 (2017)

    Article  Google Scholar 

  8. Bernard, S., Adam, L.: Using random forests for handwritten digit recognition. In: Ninth International Conference on Document Analysis and Recognition, vol. 2, pp. 1043–1047 (2007)

    Google Scholar 

  9. Breiman, L.: Random forests statistics. Mach. Learn. 45, 5–32 (2001). Department, University of California, Berkeley

    Article  MATH  Google Scholar 

  10. Zamani, Y., Souri, Y., Rashidi, H., Kasaei, S.: Persian handwritten digit recognition by random forest and convolutional neural networks. In: Conference on Machine Vision and Image Processing (2015)

    Google Scholar 

  11. Belgiu, M., Dragut, L.: Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogrammetry Remote Sens. 114, 24–31 (2016)

    Article  Google Scholar 

  12. Bernard, S., Adam, S., Heutte, L.: Dynamic random forests. Pattern Recogn. Lett. 33(12), 1580–1586 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LATIS Research Lab, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia

    Khaoula Jayech & Mohamed Ali Mahjoub

Authors
  1. Khaoula Jayech
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ali Mahjoub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khaoula Jayech .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jayech, K., Mahjoub, M.A. (2017). Object Recognition Based on Dynamic Random Forests and SURF Descriptor. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68935-7_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation