Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 264–280Cite as

Random Forest with Suppressed Leaves for Hough Voting

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10113))

Abstract

Random forest based Hough-voting techniques have been widely used in a variety of computer vision problems. As an ensemble learning method, the voting weights of leaf nodes in random forest play critical role to generate reliable estimation result. We propose to improve Hough-voting with random forest via simultaneously optimizing the weights of leaf votes and pruning unreliable leaf nodes in the forest. After constructing the random forest, the weight assignment problem at each tree is formulated as a L0-regularized optimization problem, where unreliable leaf nodes with zero voting weights are suppressed and trees are pruned to ignore sub-trees that contain only suppressed leaves. We apply our proposed techniques to several regression and classification problems such as hand gesture recognition, head pose estimation and articulated pose estimation. The experimental results demonstrate that by suppressing unreliable leaf nodes, it not only improves prediction accuracy, but also reduces both prediction time cost and model complexity of the random forest.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)

    Article  MATH  Google Scholar 

  2. Ballard, D.H.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recogn. 13, 111–122 (1981)

    Article  MATH  Google Scholar 

  3. Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 17–32 (2004)

    Google Scholar 

  4. Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1022–1029. IEEE (2009)

    Google Scholar 

  5. Keskin, C., Kıraç, F., Kara, Y.E., Akarun, L.: Hand pose estimation and hand shape classification using multi-layered randomized decision forests. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 852–863. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_61

    Chapter  Google Scholar 

  6. Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. Int. J. Comput. Vis. 101, 437–458 (2013)

    Article  Google Scholar 

  7. Yang, H., Patras, I.: Sieving regression forest votes for facial feature detection in the wild. In: IEEE International Conference on Computer Vision, pp. 1936–1943. IEEE (2013)

    Google Scholar 

  8. Xu, C., Cheng, L.: Efficient hand pose estimation from a single depth image. In: IEEE International Conference on Computer Vision, pp. 3456–3462. IEEE (2013)

    Google Scholar 

  9. Sirmaçek, B., Ünsalan, C.: Road network extraction using edge detection and spatial voting. In: International Conference on Pattern Recognition, pp. 3113–3116. IEEE (2010)

    Google Scholar 

  10. Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: IEEE International Conference on Computer Vision, pp. 415–422. IEEE (2011)

    Google Scholar 

  11. Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1038–1045. IEEE (2009)

    Google Scholar 

  12. Wohlhart, P., Schulter, S., Köstinger, M., Roth, P.M., Bischof, H.: Discriminative hough forests for object detection. In: British Machine Vision Conference, pp. 1–11(2012)

    Google Scholar 

  13. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  14. Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055 (2010)

  15. Ren, Z., Yuan, J., Meng, J., Zhang, Z.: Robust part-based hand gesture recognition using kinect sensor. IEEE Trans. Multimedia 15, 1110–1120 (2013)

    Article  Google Scholar 

  16. Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial features. In: ICPR International Workshop on Visual Observation of Deictic Gestures, Citeseer (2004)

    Google Scholar 

  17. Qian, C., Sun, X., Wei, Y., Tang, X., Sun, J.: Realtime and robust hand tracking from depth. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1106–1113. IEEE (2014)

    Google Scholar 

  18. Schulter, S., Leistner, C., Wohlhart, P., Roth, P.M., Bischof, H.: Alternating regression forests for object detection and pose estimation. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 417–424. IEEE (2013)

    Google Scholar 

  19. Rota Bulo, S., Kontschieder, P.: Neural decision forests for semantic image labelling. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 81–88. IEEE (2014)

    Google Scholar 

  20. Hara, K., Chellappa, R.: Growing regression forests by classification: applications to object pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 552–567. Springer, Cham (2014). doi:10.1007/978-3-319-10605-2_36

    Google Scholar 

  21. Ren, S., Cao, X., Wei, Y., Sun, J.: Global refinement of random forest. In: The IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  22. Gould, N., Toint, P.L.: Preprocessing for quadratic programming. Math. Program. Series B 100, 95–132 (2004)

    MathSciNet  MATH  Google Scholar 

  23. Fisher, N.I.: Statistical Analysis of Circular Data. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  24. Herdtweck, C., Curio, C.: Monocular car viewpoint estimation with circular regression forests. In: IEEE Intelligent Vehicles Symposium (2013)

    Google Scholar 

  25. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. PAMI 24, 603–619 (2002)

    Article  Google Scholar 

  26. Zhang, C., Yang, X., Tian, Y.: Histogram of 3D facets: a characteristic descriptor for hand gesture recognition. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–8. IEEE (2013)

    Google Scholar 

  27. Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2821–2840 (2013)

    Article  Google Scholar 

  28. Liang, H., Yuan, J., Thalmann, D.: 3D fingertip and palm tracking in depth image sequences. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 785–788. ACM (2012)

    Google Scholar 

  29. Zhang, C., Tian, Y.: Histogram of 3D facets: a depth descriptor for human action and hand gesture recognition. Comput. Vis. Image Underst. 139, 29–39 (2015)

    Article  Google Scholar 

  30. Lai, Z., Yao, Z., Wang, C., Liang, H., Chen, H., Xia, W.: Fingertips detection and hand gesture recognition based on discrete curve evolution with a kinect sensor. In: Visual Communications and Image Processing (2016)

    Google Scholar 

  31. Haj, M.A., Gonzalez, J., Davis, L.S.: On partial least squares in head pose estimation: How to simultaneously deal with misalignment. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2602–2609. IEEE (2012)

    Google Scholar 

  32. Fenzi, M., Leal-Taixé, L., Rosenhahn, B., Ostermann, J.: Class generative models based on feature regression for pose estimation of object categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 755–762 (2013)

    Google Scholar 

  33. Sun, X., Wei, Y., Liang, S., Tang, X., Sun, J.: Cascaded hand pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 824–832 (2015)

    Google Scholar 

  34. Tang, D., Chang, H.J., Tejani, A., Kim, T.K.: Latent regression forest: structured estimation of 3D articulated hand posture. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3786–3793. IEEE (2014)

    Google Scholar 

Download references

Acknowledgement

This work is supported in part by Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-114 and Tier 1 RG27/14.

Author information

Authors and Affiliations

  1. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Hui Liang, Junhui Hou & Junsong Yuan

  2. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Daniel Thalmann

Authors
  1. Hui Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junhui Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junsong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Thalmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Liang .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo , Tokyo, Japan

    Yoichi Sato

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Liang, H., Hou, J., Yuan, J., Thalmann, D. (2017). Random Forest with Suppressed Leaves for Hough Voting. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54187-7_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54186-0

  • Online ISBN: 978-3-319-54187-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation