Scholarlink

<p>Image databases are central to empirical aesthetics, enabling tests of how image statistics relate to observers’ appreciation. However, many existing databases have two key limitations: (1) they conflate low-level visual features with high-level semantic content, making it difficult to separate visual from cognitive influences on aesthetic judgments; and (2) they are imbalanced, overrepresenting highly appreciated images. To address these issues, we present the Minimum Semantic Content (MSC) database, a large, systematically curated resource for computational aesthetics. It comprises 10,426 natural scenes with reduced, homogenized semantic content, minimizing cognitive and emotional confounds. Each received 100 individual aesthetic ratings from naïve observers, drawn from a pool of approximately 10,000 participants, via crowdsourcing. The database includes both “beautified” and “uglified” versions, generated with a manipulation technique that promotes uniform coverage across the aesthetic spectrum. This broader distribution mitigates bias and overfitting in models. Validation also shows improved robustness in computational models overall. This database enables researchers to study how perceptual features shape aesthetic judgments, using stimuli with very limited semantic and contextual confounds.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The Minimum Semantic Content (MSC) Dataset: A Large, Balanced Resource for Computational Aesthetics Research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月17日

查看全文
  • Olivier Penacchio,
  • Arslan Javed,
  • Bogdan Raducanu,
  • Xavier Otazu,
  • C. Alejandro Parraga

摘要

Image databases are central to empirical aesthetics, enabling tests of how image statistics relate to observers’ appreciation. However, many existing databases have two key limitations: (1) they conflate low-level visual features with high-level semantic content, making it difficult to separate visual from cognitive influences on aesthetic judgments; and (2) they are imbalanced, overrepresenting highly appreciated images. To address these issues, we present the Minimum Semantic Content (MSC) database, a large, systematically curated resource for computational aesthetics. It comprises 10,426 natural scenes with reduced, homogenized semantic content, minimizing cognitive and emotional confounds. Each received 100 individual aesthetic ratings from naïve observers, drawn from a pool of approximately 10,000 participants, via crowdsourcing. The database includes both “beautified” and “uglified” versions, generated with a manipulation technique that promotes uniform coverage across the aesthetic spectrum. This broader distribution mitigates bias and overfitting in models. Validation also shows improved robustness in computational models overall. This database enables researchers to study how perceptual features shape aesthetic judgments, using stimuli with very limited semantic and contextual confounds.