构建最小语义内容图像库，揭示美学判断中语义干扰的误导性。
Aesthetics Without Semantics
- 创建含10426张低语义图像的平衡数据库，每图经100人评估。
- 用丑图补充美图数据集后，图像特征与美学评分关系被反转。
- 提醒研究者：仅用美图易误判美学规律，适合关注认知偏差的研究者。
人类虽能轻松判断图像美丑，但审美决策受感知与认知（语义）因素交织影响，科学理解难度大。现有数据库普遍偏向美丽图像，加剧研究难度。为此，我们构建了最小语义内容（MSC）数据库，包含10,426张低语义图像，每幅由100名观察者评价。利用经典图像指标发现，向偏美的数据集添加丑图后，图像特征与美学评分的关系可被改变甚至反转。研究显示，当前实证美学研究若仅考虑有限的审美范围，可能夸大、低估或遗漏真实效应。
原文摘要 · Abstract (English)
While it is easy for human observers to judge an image as beautiful or ugly, aesthetic decisions result from a combination of entangled perceptual and cognitive (semantic) factors, making the understanding of aesthetic judgements particularly challenging from a scientific point of view. Furthermore, our research shows a prevailing bias in current databases, which include mostly beautiful images, further complicating the study and prediction of aesthetic responses. We address these limitations by creating a database of images with minimal semantic content and devising, and next exploiting, a method to generate images on the ugly side of aesthetic valuations. The resulting Minimum Semantic Content (MSC) database consists of a large and balanced collection of 10,426 images, each evaluated by 100 observers. We next use established image metrics to demonstrate how augmenting an image set biased towards beautiful images with ugly images can modify, or even invert, an observed relationship between image features and aesthetics valuation. Taken together, our study reveals that works in empirical aesthetics attempting to link image content and aesthetic judgements may magnify, underestimate, or simply miss interesting effects due to a limitation of the range of aesthetic values they consider.
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