Study on learning techniques which utilize existing classifiers
| Project/Area Number |
23500173
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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 |
Intelligent informatics
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| Research Institution | Shinshu University |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2011: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 知能情報処理 / パターン認識 / 画像認識 / 機械学習 / 類似度計算 / 領域分割 / 画像検索 / 類似度検索 / 画像識別 / SVM / 領域抽出 / カテゴリ認識 / 確率モデル / 特徴抽出 |
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| Research Abstract |
We have investigated methods to utilize still existing classifiers to construct classifier for a novel category. We have studied the methods for similar image search and image segmentation problems. For image search, given a query image, appropriate weight vector is obtained via learning from examples. Although the search quality of the method is very good, it is time consuming. To reduce the computational cost, we proposed a method which approximates weight vectors from existing classifiers (weight vectors). We compared the search quality with the method based on the dimensionality reduction and found PCA-based method can outperform the other approximation techniques. For image segmentation, the task is formulated as the energy minimization. We tried to improve the energy function using the existing image classifier. We have shown the segmentation quality can be improved by limiting possible object categories.
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Report
(4 results)
Research Products
(25 results)
