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
Author(s)
Detail(s)
Original language | English |
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Title of host publication | Computer Vision |
Subtitle of host publication | 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers |
Editors | Yoichi Sato, Shang-Hong Lai, Ko Nishino, Vincent Lepetit |
Publisher | Springer Verlag |
Pages | 264-280 |
Volume | 10113 LNCS |
ISBN (Print) | 9783319541860 |
Publication status | Online published - Mar 2017 |
Externally published | Yes |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10113 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Link(s)
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 et al.
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