A study on image representation by hierarchical probabilistic models and its applications
| Project/Area Number |
20500129
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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 | Shinshu University |
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Principal Investigator |
MARUYAMA Minoru Shinshu University, 工学部, 准教授 (80283232)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2009: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2008: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 画像認識 / 確率的トピックモデル / パターン識別 / 識別関数 / pLSA / 確率モデル / トピックモデル / 文書分類 / 機械学習 / 画像識別 / pLSAモデル / 階層型識別器 / 文書画像処理 / boosting / 領域分割 / 画像特徴抽出 |
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| Research Abstract |
We have mainly studied about hierarchical probabilistic model for image representation ant its application to category recognition. In our method, we used probabilistic topic model. We proposed a method for classifying regions in a document image based on pLSA (probabilistic latent semantic analysis). Classification is carried out based on topic proportion of the region that is estimated via MAP-based EM algorithm. The model is further extended to classify smaller image regions. We also presented the method to detect characters in a natural scene images by using decision stumps. A part of this research work received best paper award honorable mention at the IAPR workshop on Document Analysis Systems 2008.
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Report
(4 results)
Research Products
(12 results)
