Random forest with suppressed leaves for hough voting

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationComputer Vision
Subtitle of host publication13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers
EditorsYoichi Sato, Shang-Hong Lai, Ko Nishino, Vincent Lepetit
PublisherSpringer Verlag
Pages264-280
Volume10113 LNCS
ISBN (Print)9783319541860
Publication statusOnline published - Mar 2017
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10113 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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.

Citation Format(s)

Random forest with suppressed leaves for hough voting. / Liang, Hui; Hou, Junhui; Yuan, Junsong; Thalmann, Daniel.

Computer Vision: 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers. ed. / Yoichi Sato; Shang-Hong Lai; Ko Nishino; Vincent Lepetit. Vol. 10113 LNCS Springer Verlag, 2017. p. 264-280 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10113 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review