AQC0509

Nanopublication — Computational Image Analysis - AQC0509

by Arnaud Quercy · Le chant du Chardonneret élégant · 2024

Claim 1: Computational Image Analysis - AQC0509

K-means clustering analysis [3] (10 colors) performed on artwork Le chant du Chardonneret [1] élégant (AQC0509) [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]: 1366x2048 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 C3C0AC 15.3 yellow silver
2 474137 11.6 yellow-orange darkslategray
3 CDAC72 11.2 yellow-orange ochre
4 A48B5D 10.9 yellow-orange peru
5 969B93 10.6 yellow-green steel gray
6 E1DECF 10.5 yellow gainsboro
7 6C6E67 10.3 gray dimgray
8 1F1912 8.4 orange black
9 8B5926 6.7 orange burnt sienna
10 C87E37 4.4 orange chocolate
11 30426B 0.3 blue-violet grayish purple [Accent]

Color Families:

Family %
yellow-orange 33.8
yellow 25.9
orange 19.5
yellow-green 10.6
gray 10.3
blue-violet 0.3

Accent Colors:

Hex Family Name Chroma
30426B blue-violet grayish purple 26.7

Texture Analysis

Metric Value
Global Roughness 0.229
Mean Local Roughness 0.052
Roughness Uniformity 0.025
Edge Density 0.317
Mean Gradient Magnitude 0.392
Gradient Variance 0.11
Gradient Smoothness 0.153
Directional Coherence 0.001
Pattern Complexity 0.118
Pattern Repetition 1.0
Detail Frequency Ratio 0.64
Spatial Variation 0.082
Texture Consistency 0.777

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.542
Brightness Variance 0.229
Brightness Uniformity 0.578
Brightness Skewness -0.402
Brightness Entropy 7.79
Rms Contrast 0.229
Michelson Contrast 1.0
Weber Contrast 0.757
Mean Local Contrast 0.052
Contrast Uniformity 0.52
Dynamic Range 1.0
Effective Dynamic Range 0.753
Shadow Percentage 20.491
Midtone Percentage 45.091
Highlight Percentage 34.418
Shadow Clipping 0.106
Highlight Clipping 0.025
Tonal Balance 0.48
Fine Contrast 0.031
Medium Contrast 0.065
Coarse Contrast 0.088
Multiscale Contrast Ratio 0.357
Edge Contrast 0.392
Contrast Clustering 0.223

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.666
Color Clustering 0.795
Color Transition Smoothness 0.0
Transition Uniformity 0.213
Sharp Transition Ratio 0.1
Transition Directionality 0.002
Mean Saturation 0.313
Saturation Variance 0.061
Low Saturation Ratio 0.567
Medium Saturation Ratio 0.336
High Saturation Ratio 0.097
Saturation Clustering 0.995
Hue Concentration 0.857
Complementary Balance 0.053
Analogous Dominance 0.92
Temperature Bias 0.818

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). Le chant du Chardonneret élégant — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0509.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/le-chant-du-chardonneret-elegant_5q6.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)

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