AQC0518

Nanopublication — Computational Image Analysis - AQC0518

Claim 1: Computational Image Analysis - AQC0518

K-means clustering analysis [3] (10 colors) performed on artwork Promenade [1] aux jardins du Luxembourg -variations d'été - Variation 2 (AQC0518) [2] by Arnaud Quercy [2] on 2026-02-04. Documentation includes: color families, texture roughness, brightness distribution, spatial coherence.

Context

Analysis performed according to MMIDS-CMP-2025 [3] includes four metric categories: (1) Color distribution via k-means (10 colors), (2) Texture analysis using Haralick features, (3) Brightness and contrast measurements, (4) Spatial pattern characterization. Source image [5]: 2258x3236 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 E3D9CB 18.7 yellow-orange gainsboro
2 7C2B20 14.0 red-orange russet
3 332121 12.3 red-orange very dark gray
4 E98013 10.7 orange darkorange
5 BA3A36 9.1 red-orange brown
6 52303D 8.8 red dusty mauve
7 CC5556 8.0 red-orange indianred
8 8B4F55 7.8 red-orange burnt sienna
9 E3A681 5.3 orange darksalmon
10 B27A80 5.3 red rosybrown
11 6F4283 0.3 red-violet darkslateblue [Accent]
12 FDFAEA 0.3 yellow white [Accent]

Color Families:

Family %
red-orange 51.2
yellow-orange 18.7
orange 16.0
red 14.1
red-violet 0.3
yellow 0.3

Accent Colors:

Hex Family Name Chroma
6F4283 red-violet darkslateblue 42.4
FDFAEA yellow white 8.2

Texture Analysis

Metric Value
Global Roughness 0.244
Mean Local Roughness 0.026
Roughness Uniformity 0.03
Edge Density 0.113
Mean Gradient Magnitude 0.211
Gradient Variance 0.094
Gradient Smoothness 0.0
Directional Coherence 0.028
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.629
Spatial Variation 0.126
Texture Consistency 0.589

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.468
Brightness Variance 0.244
Brightness Uniformity 0.479
Brightness Skewness 0.411
Brightness Entropy 7.572
Rms Contrast 0.244
Michelson Contrast 1.0
Weber Contrast 0.789
Mean Local Contrast 0.028
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.729
Shadow Percentage 37.314
Midtone Percentage 39.202
Highlight Percentage 23.484
Shadow Clipping 0.003
Highlight Clipping 0.006
Tonal Balance 0.257
Fine Contrast 0.014
Medium Contrast 0.035
Coarse Contrast 0.053
Multiscale Contrast Ratio 0.263
Edge Contrast 0.211
Contrast Clustering 0.411

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.721
Color Clustering 0.586
Color Transition Smoothness 0.461
Transition Uniformity 0.398
Sharp Transition Ratio 0.1
Transition Directionality 0.029
Mean Saturation 0.492
Saturation Variance 0.074
Low Saturation Ratio 0.281
Medium Saturation Ratio 0.435
High Saturation Ratio 0.284
Saturation Clustering 0.999
Hue Concentration 0.952
Complementary Balance 0.0
Analogous Dominance 0.985
Temperature Bias 0.991

Methodology

This analysis employs standardized computational methods for objective image characterization. Color extraction uses k-means clustering algorithm. Texture analysis applies Haralick feature extraction. Brightness metrics include mean, variance, and distribution analysis. Spatial patterns are characterized through coherence and clustering measurements. All methods are deterministic and reproducible. Analysis performed by Multimodal Institute's computational imaging systems.

References

[1] Arnaud Quercy (2024). Promenade aux jardins du Luxembourg -variations d'été - Variation 2 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0518.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/promenade-aux-jardins-du-luxembourg-variations-dete-variation-2_5to.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/11/mmids-cmp-2025-computational-image-analysis-standard-dg1.html

Epistemic profile

Claim typecomputational analysis
Voicethird person
Epistemic statusempirical measurement
Methodologycomputational analysis
Certaintyhigh

Checksum (SHA-256)

054364838b636425d9aebc0367b738b7e259ef8a429e548200dd0b27ab38865d