Graph-based semi-supervised learning with active contour model
| Project/Area Number |
24700170
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
DU Weiwei 京都工芸繊維大学, 工芸科学研究科, 助教 (00512790)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2013: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 半教師付き学習 / パターン認識 / 医用画像 / 動的輪郭モデル / 特徴ベクトルノイズ / クラスラベルノイズ / 動的輪郭モデル初期値 / 動的輪郭モデルパラメータ |
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| Research Abstract |
This is a difficult problem on how to classify the unlabeled data in training data and the test data by few labeled data in the field of machine learning and pattern recognition. Especially, some noise data lie on the training data. We propose a graph-based semi-supervised learning with active contour based on the above problems. The algorithm not only removes the noise data, but also classifies the unlabeled data in training data and the test data. The algorithm is applied in the synthetic data and medical images.
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Report
(3 results)
Research Products
(3 results)
