| Project/Area Number |
15K00151
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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 |
Multimedia database
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
Kuriyama Shigeru 豊橋技術科学大学, 工学(系)研究科(研究院), 教授 (20264939)
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| Co-Investigator(Renkei-kenkyūsha) |
OHBUCHI Ryutarou 山梨大学, 大学院総合研究部, 教授 (80313782)
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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: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 画像検索 / イラスト画像 / 描画スタイル / スタイル識別子 / 特徴分類 / 教師無し機械学習 / 局所特徴量 / Fisher Vector / 作画スタイル / 次元削減 / 機械学習 / スタイル特徴量 / 色彩理論 / 画像セグメンテーション / 画像特徴分類 |
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| Outline of Final Research Achievements |
This research proposed a block-wise local discriminator for drawing styles of illustration images. The bags-of-features for styles are coded to compose a dictionary, which is utilized to generate fisher vectors. We reduced their dimensions by introducing non-linear, non-supervised manifold learning for improving the performance.
Through the comparative evaluation of discrimination accuracy, we have found that our method has superior performance in discriminating styles, and we received six kinds of awards from both domestic academic society and international symposium, which shows the highly-evaluated contribution of our method.
Our style-discriminators can be utilized for the retrieval, visualization, and transfer of drawing styles to improve the design of illustrative drawings, for a large scale of image data set such as clip-art archives.
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