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Updated: Feb 19, 2026

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最小セマンティック・コンテンツ (MSC) データセット: コンピューティング・エステティクス研究のための大規模でバランスの取れたリソース
Olivier Penacchio1,2,3, Arslan Javed4,5, Bogdan Raducanu4,5
1Computer Science Dept., Engineering School, Universitat Autònoma de Barcelona (UAB), Campus UAB, Bellaterra, 08193, Barcelona, Spain. penacchio@cvc.uab.cat.
Scientific data
|February 17, 2026
まとめ
新しいMinimum Semantic Content (MSC) データベースは,制御された意味論的内容を持つ自然シーンを提供することによって,経験的美学研究を支援しています. このリソースは,美学的な判断に対する認知的な影響から視覚的な特徴を分離するのに役立ちます.
科学分野:
- 経験的美学とは
- 計算神経科学とは
- コンピュータビジョン コンピュータビジョン
背景:
- 画像データベースは,経験的美学にとって不可欠ですが,しばしば視覚的特徴と意味論的コンテンツを混同します.
- 既存のデータベースはしばしば不均衡であり,非常に評価された画像を過剰に表現し,研究に偏りがあります.
- これは,美学的な判断に対する知覚の影響を隔離する能力を制限する.
研究 の 目的:
- 最小セマンティックコンテンツ (MSC) データベースを導入し,コンピューティングの美学のための新しいリソースである.
- セマンティックと認知の混乱を最小限に抑えることで,既存のデータベースの限界に対処します.
- 視覚的特徴と美学的な評価の関係に関する研究を促進する.
主な方法:
- 自然景観の大規模なデータベース (10,426 画像) を開発し,意味論的な内容を減少させ,均一化しました.
- クラウドソーシング (画像1枚につき100点) を通じて,約1万人の参加者から美学評価を集めました.
- 均一なエステティックスペクトルのカバーを確保するために,生成された"美化"および"醜化"の画像バージョン.
主要な成果:
- MSCのデータベースは,美学的な判断の研究における認知的,感情的混乱を最小限に抑えています.
- キュレーションされた画像セットは,美学的なスペクトル全体で均一なカバーを促進し,バイアスを軽減します.
- 検証により,コンピューティングモデルの強度と性能が向上したことが示されました.
結論:
- MSCデータベースは,経験的美学研究のための貴重な,体系的にキュレーションされたリソースを提供します.
- これは,研究者が,感知特性の美学的な判断への影響を,混乱を軽減して調査することを可能にします.
- このリソースは,より堅牢な計算モデルの開発を進めます.
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