Compression-based self-organizing Recognizer Design
| Project/Area Number |
22500122
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
WATANABE Toshinori 電気通信大学, 大学院・情報システム学研究科, 教授 (10242348)
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| Co-Investigator(Kenkyū-buntansha) |
KOGA Hisashi 電気通信大学, 大学院・情報システム学研究科, 准教授 (40361836)
CHO Daku 電気通信大学, 大学院・情報システム学研究科, 助教 (20436736)
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2010: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | パターン認識 / 機械知能 / マイニング / ウェブ分析 / 自律システム / 画像解析 |
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| Research Abstract |
Traditional object recognition schemes have been the statistical one wherein target objects’ statistical models are prepared manually and applied to unknown task data. Due to the heavy human intervention, this approach becomes weak for multi-media data with variety of target objects. In this research, a new autonomous object model acquisition scheme is investigated. For this, we investigate the possibility of data’s compressibility vector as a general feature, the possibility of co-occurrence-based object discovery and the possibility of highly autonomous recognition scheme based oh them. Promising experimental results are reported.
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Report
(4 results)
Research Products
(21 results)
