Adaptive Content-based Image Retrieval using Semi-Supervised Learning Method
| Project/Area Number |
22500152
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | Nagoya University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
NOBORU Ohnishi 名古屋大学, 情報科学研究科, 教授 (70185338)
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| Project Period (FY) |
2010-10-20 – 2013-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 画像情報処理 / 類似画像検索 / 部分教師付学習 / 適応的画像内容検索 |
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| Research Abstract |
Automatic image annotation is a hopeful sub-technique for image database retrieval. We have been constructing a generative model system for automatic image annotation using semi-supervised learning method. As it can be easily unstable for the higher dimensions, we must apply a dimensionality reduction method in advance. Generally, conventional supervised dimensionality reduction method (using labeled samples) suffers from the degenerate covariance matrix problem in the case of a small number of samples. On the other hand, unsupervised dimensionality reduction method (using unlabeled samples) can't recognize the differences among the categories properly. In this study, we propose a novel semi-supervised dimensionality reduction method using a small number of labeled samples and a large number of unlabeled samples. By the result of experiments, the classification rate of the proposed method was 5.1 points better than that of the unsupervised method.
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Report
(4 results)
Research Products
(15 results)
