Aesthetics Without Semantics
C. Alejandro Parraga (1, 2), Olivier Penacchio (1, 2, 3), Marcos Mu\v{n}oz Gonzalez (1), Bogdan Raducanu (2), Xavier Otazu (1, 2) ((1) Comp. Sci. Dept., Engineering School, Universitat Aut\`onoma de Barcelona (UAB), Campus UAB-Bellaterra, 08193, Barcelona, Spain

TL;DR
This study introduces a new image database with minimal semantic content and a method to generate ugly images, enabling better understanding of aesthetic judgments independent of semantic influence.
Contribution
The paper presents the MSC database of over 10,000 images and a technique for generating aesthetically ugly images, addressing biases in existing datasets and advancing aesthetic research.
Findings
Augmenting datasets with ugly images alters feature-aesthetics relationships.
Bias towards beautiful images in datasets can distort aesthetic analysis.
Including diverse aesthetic values reveals complex feature-judgment interactions.
Abstract
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…
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Taxonomy
TopicsAesthetic Perception and Analysis · Visual Attention and Saliency Detection · Ethics, Aesthetics, and Art
MethodsSparse Evolutionary Training
