Evidence map›Paper›PMID 34460522›Full record

ArticleJournal of imaging2021

Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings.

Juan Luis Nieves, Juan Ojeda, Luis Gómez-Robledo, Javier Romero

Abstract read
In one paragraph

Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Juan Luis NievesDepartment of Optics, Faculty of Science, University of Granada, 18071 Granada, Spain.ORCID 0000-0002-3103-8322
Juan OjedaDepartment of Optics, Faculty of Science, University of Granada, 18071 Granada, Spain.
Luis Gómez-RobledoDepartment of Optics, Faculty of Science, University of Granada, 18071 Granada, Spain.ORCID 0000-0002-6287-1632
Javier RomeroDepartment of Optics, Faculty of Science, University of Granada, 18071 Granada, Spain.

Funding

Ministerio de Ciencia e Innovación RTI2018-094738-B-I00
6 · The paper itself

Abstract

In an early study, the so-called "relevant colour" in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to analyse how observers determine the relevant colours by describing observers' subjective impressions of the most representative colours in paintings and to provide a psychophysical backing for a related computational model we proposed in a previous work. This subjective impression is elicited by an efficient and optimal processing of the most representative colour instances in painting images. Our results suggest an average number of 21 subjective colours. This number is in close agreement with the computational number of relevant colours previously obtained and allows a reliable segmentation of colour images using a small number of colours without introducing any colour categorization. In addition, our results are in good agreement with the directions of colour preferences derived from an independent component analysis. We show that independent component analysis of the painting images yields directions of colour preference aligned with the relevant colours of these images. Following on from this analysis, the results suggest that hue colour components are efficiently distributed throughout a discrete number of directions and could be relevant instances to a priori describe the most representative colours that make up the colour palette of paintings.

Indexed as

colourcolour visionpaintings

Identifiers

PMID34460522
PMCPMC8321366

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.