2022 Fiscal Year Final Research Report
A Study on Deep Learning of Unsupervised Image Segmentation by Differentiable Clustering
| Project/Area Number |
20K19837
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61010:Perceptual information processing-related
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Kanezaki Asako 東京工業大学, 情報理工学院, 准教授 (00738073)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 画像処理 / 深層学習 / 教師なし学習 / 画像セグメンテーション |
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| Outline of Final Research Achievements |
In this study, we developed an unsupervised deep learning image segmentation method that does not require supervised data. Compared to graph cuts, the de facto standard unsupervised image segmentation method, and recently developed deep learning-based conventional methods, the proposed method is highly effective on several benchmark datasets. The research results have been accepted for publication in IEEE TIP (IF: 9.34), a top journal in the field of image processing. Furthermore, we received the Telecommunications Advancement Foundation Telecom System Technology Student Award and IEEE Signal Processing Society (SPS) Japan Student Journal Paper Award.
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| Free Research Field |
知能情報処理
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果を再現するソースコードをオープンソースとしてGitHubに公開しており，既に600弱のスター数を獲得している．当該ソースコードは，世界中の様々な大学や研究機関において，特に医用画像処理分野で広く利用されている．さらに，「教師なし画像セグメンテーションのベーシックな手法と深層学習ベースの手法の紹介」という論文タイトルで，日本医用画像工学会(JAMIT)誌「MEDICAL IMAGING TECHNOLOGY 39(4)」の特集論文を寄稿した．
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