| Project/Area Number |
15K00248
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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 | Waseda University |
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Principal Investigator |
Kamata Seiichiro 早稲田大学, 理工学術院(情報生産システム研究科・センター), 教授 (00204602)
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| Research Collaborator |
SUGIMOTO Kenjiro
ZHANG Qieshi
RYU Jegoon
HAO Pengyi
WU Renjie
OKUTANI Ryo
YE Xiaoxi
LAI Yongwen
LIM Xueting
QIU Fan
TANG Kaihua
WU Xinhui
NI Shoucheng
YANO Koichi
TIAN Li
MA Lizhuang
BRECKON Toby
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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 |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 画像検索 / 画像認識 / スパースグラフ / ニューラルネットワーク / ビジュアルビッグデータ / ディープラーニング / 圧縮可能性 / 基底関数分解 / 深層学習 / ハッシュ関数 / グラフニューラルネットワーク / 畳込みニューラルネットワーク / 高速画像検索 / 高速画像認識 / 定数時間フィルタ / 高速特徴記述 / 高速特徴点検出 |
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| Outline of Final Research Achievements |
Big data from visual media, which is called visual big data, was utilized in this study, and new methodologies in image retrieval and recognition were established based on a concept of compressibility which is focusng on reduction of computational complexity in information theory. Especially combining with state-of-the-art deep learning, a new research direction of sparse graph neural network was developed using face visual big data.
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