| Project/Area Number |
15K00253
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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 |
Perceptual information processing
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| Research Institution | Yamaguchi University (2016-2017)
Ritsumeikan University (2015) |
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Principal Investigator |
Han Xian-HUa 山口大学, 大学院創成科学研究科, 准教授 (60469195)
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| Co-Investigator(Kenkyū-buntansha) |
陳 延偉 立命館大学, 情報理工学部, 教授 (60236841)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 画像認識 / 機械学習 / コンピュータビジョン / パターン認識 / 画像処理 / 画像表現 / High-level特徴学習 / 高レベル画像表現 / 人間認知理論 / K-Supportプーリング法 / Stacked Fisher Network / CNN / 国際情報交換 |
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| Outline of Final Research Achievements |
This study aimed at learning compact and inherent image representation for high-level vision probelms, and developed advanced image recognition and understanding methods. Our main achievements are three-fold: 1) Based on human’s perception principle, we transformed the raw-image domain into differential excitation domain and proposed to use the micro-texton as local descriptors for retaining all information, which would be distinguishable even for the subtle difference in image structures. 2) We proposed a novel middle-level image representation learning framework via exploring the deviation statistics of local descriptor set on the learned GMM model; 3) We stacked several layers of the middle level representation extraction framework, and proposed multiple-layer fisher network architecture for high-level feature learning. We applied our proposed image representation learning strategy for several image recognition applications, and proved much better performances can be achieved.
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