Automatic Construction of Feature Extraction Process Based on Combinatorial Optimization of Image Processing Filters
Project/Area Number |
15K16029
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Perceptual information processing
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Research Institution | Yokohama National University (2016-2017) University of Tsukuba (2015) |
Principal Investigator |
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Project Period (FY) |
2015-04-01 – 2018-03-31
|
Project Status |
Completed (Fiscal Year 2017)
|
Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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Keywords | 画像認識 / 画像特徴量 / 機械学習 / 進化計算 / 画像処理 |
Outline of Final Research Achievements |
In this research, we developed a novel technique that constructs efficient image feature extraction process by using a black-box optimization method based on combinatorial optimization of basic image processing units such as image processing filters. Further, we extended and applied the idea of this technique to convolutional neural networks (CNN) that show high performance on image recognition tasks. From the numerical experiments of image recognition, we confirmed that the proposed methods outperform the existing CNN based methods.
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Report
(4 results)
Research Products
(14 results)