Presentation 2007-12-13
Pose Recognition with the NNC-Tree
Jie Ji, Naoki Tominaga, Akiha Iwase, Yoshihiko Watanabe, Kazuhiko Hirakuri, Qiangfu Zhao,
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Abstract(in English) Pose recognition is important in many practical applications. For example, a driver assistance system can detect if the driver is tired, sleepy, or careless from the poses. A pet robot can detect certain behavior patterns of the human user. In this study, we developed a system for recognizing different poses of a human from the face images by using NNC-Tree. An NNC-Tree is a decision tree (DT) with each internal node containing a nearest neighbor classifier (NNC). We provide a GUI for visualizing the prototypes in each NNC, as well as the whole tree. This interface makes it possible to understand, analysis and reuse the learning results. Dimensionality reduction is also employed to reduce the computational cost for induction, and the complexity of the tree.
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Keyword(in English) Pattern recognition / pose recognition / nearest neighbor classifier / decision tree / NNC-Tree
Paper # PRMU2007-142
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Conference Information
Committee PRMU
Conference Date 2007/12/6(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Pose Recognition with the NNC-Tree
Sub Title (in English)
Keyword(1) Pattern recognition
Keyword(2) pose recognition
Keyword(3) nearest neighbor classifier
Keyword(4) decision tree
Keyword(5) NNC-Tree
1st Author's Name Jie Ji
1st Author's Affiliation The University of Aizu()
2nd Author's Name Naoki Tominaga
2nd Author's Affiliation The University of Aizu
3rd Author's Name Akiha Iwase
3rd Author's Affiliation The University of Aizu
4th Author's Name Yoshihiko Watanabe
4th Author's Affiliation The University of Aizu
5th Author's Name Kazuhiko Hirakuri
5th Author's Affiliation The University of Aizu
6th Author's Name Qiangfu Zhao
6th Author's Affiliation The University of Aizu
Date 2007-12-13
Paper # PRMU2007-142
Volume (vol) vol.107
Number (no) 384
Page pp.pp.-
#Pages 6
Date of Issue