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最小语义内容 (MSC) 数据集:计算美学研究的一个大而平衡的资源
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
概括
新的最小语义内容 (MSC) 数据库通过提供具有控制语义内容的自然场景来帮助经验美学研究. 这个资源有助于将视觉特征与对美学判断的认知影响分开.
科学领域:
- 经验的美学 经验的美学
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
背景情况:
- 图像数据库对于经验美学至关重要,但经常将视觉特征与语义内容混为一谈.
- 现有的数据库经常不平衡,过度表现出高度欣赏的图像,这对研究产生了偏见.
- 这限制了对审美判断的感知影响的隔离能力.
研究的目的:
- 引入最小语义内容 (MSC) 数据库,这是一个新的计算美学资源.
- 通过最小化语义和认知混来解决现有数据库的局限性.
- 促进研究视觉特征与美学欣赏之间的关系.
主要方法:
- 开发了一个大数据库 (10,426张图像) 的自然场景与减少和同质化语义内容.
- 通过众包收集了大约1万名参与者的审美评分 (每张图片100个评分).
- 生成"美化"和"丑化"的图像版本,以确保统一的审美范围覆盖.
主要成果:
- 在美学判断研究中,MSC数据库尽量减少认知和情感混.
- 策划的图像集促进了整个美学谱的统一覆盖,减轻了偏见.
- 验证表明计算模型的稳定性和性能有所改善.
结论:
- 该MSC数据库提供了一个有价值的,系统地策划的资源,用于实证美学研究.
- 它使研究人员能够研究感知特征对美学判断的影响,从而减少混.
- 这个资源促进了对美学更强大的计算模型的开发.
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