Presentation | 2014-12-12 A proposal for data selection in self-training based cross dataset action recognition Takafumi SUZUKI, Yu WANG, Jien KATO, Kenji MASE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In action recognition, in order to obtain high performance classifiers, it is necessary to feed the training algorithm enough labeled data. Since labeling is a very expensive task, it is necessary to develop approaches which can efficiently reuse labeled data. In this work, we consider the task of utilizing labeled data from one dataset (source dataset) to train action classifiers for data from another completely unlabeled dataset (target dataset). We propose a novel approach for such a task by extending the well-known self-training algorithm to including two different data selecting criterions that are inspired by Support Vector Machine and k-Nearest Neighbor. The superior of our approach has been confirmed by benchmark dataset. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Action Recognition / Pattern Recognition / Self Training |
Paper # | PRMU2014-80 |
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Committee | PRMU |
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Conference Date | 2014/12/4(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A proposal for data selection in self-training based cross dataset action recognition |
Sub Title (in English) | |
Keyword(1) | Action Recognition |
Keyword(2) | Pattern Recognition |
Keyword(3) | Self Training |
1st Author's Name | Takafumi SUZUKI |
1st Author's Affiliation | Nagoya University() |
2nd Author's Name | Yu WANG |
2nd Author's Affiliation | Nagoya University |
3rd Author's Name | Jien KATO |
3rd Author's Affiliation | Nagoya University |
4th Author's Name | Kenji MASE |
4th Author's Affiliation | Nagoya University |
Date | 2014-12-12 |
Paper # | PRMU2014-80 |
Volume (vol) | vol.114 |
Number (no) | 356 |
Page | pp.pp.- |
#Pages | 5 |
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