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Record W2232301194 · doi:10.2312/egsh.20141005

Towards Understanding Beautiful Things: A Computational Approach for the Study of Color Modulation in Visual Art

2014· article· en· W2232301194 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEurographics · 2014
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceModulation (music)Artificial intelligenceComputer visionAestheticsArt

Abstract

fetched live from OpenAlex

This paper is a guided attempt at analyzing the aesthetics of color from the perspective of color theory. Our guides are the works of Johannes Itten, one of the most influential theorists of color aesthetics. We focus on one specific aspect of color usage in visual art, namely color modulation. To this purpose, we introduce the color palette, a novel 3D visualization of the chromatic information of an image in the HSL space. Moreover, we propose a set of simple descriptors for evaluating color modulation. Our approach is demonstrated on two case studies, which show that our measures on modulation are consistent with Itten’s color theory. Ongoing work involves a thorough experimental exploration of the proposed color palette and modulation descriptors, in terms of their ability to discriminate between different artists and painting styles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.111
GPT teacher head0.362
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it