Universal Classifier: Adaptive Object Recognition for Multiple Domains
| Project/Area Number |
25330215
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Perceptual information processing
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| Research Institution | Kinki University |
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Principal Investigator |
HABE Hitoshi 近畿大学, 理工学部, 講師 (80346072)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 物体認識 / 性別推定 / Random Forests / 転移学習 / ランダムフォレスト / 環境適応 / 決定木 / Random Forest / 共通知識 / 個別知識 |
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| Outline of Final Research Achievements |
Image- and video-based object recognition usually requires a large amount of training data for sufficient performance. However, it is not always possible to obtain such a large dataset. Hence, we propose a method for extracting useful classifiers from a set of existing classifiers that is obtained by using training data of other scenes. Combining the extracted classifiers enables us to achieve better performance than solely using training data of a new scene. Experimental results show the effectiveness of the proposed method.
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Report
(4 results)
Research Products
(3 results)
