Random Forest with Suppressed Leaves for Hough Voting

  • Hui LiangEmail author
  • Junhui Hou
  • Junsong Yuan
  • Daniel Thalmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)


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.


Random Forest Local Feature Leaf Node Training Image Euler Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hui Liang
    • 1
    Email author
  • Junhui Hou
    • 1
  • Junsong Yuan
    • 1
  • Daniel Thalmann
    • 2
  1. 1.School of Electrical and Electronics EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Institute for Media InnovationNanyang Technological UniversitySingaporeSingapore

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