AQC0647

Nanopublication — Computational Image Analysis - AQC0647

by Arnaud Quercy · A Major - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0647

Computational image analysis [3] of artwork A Major [1] - Research on Harmony (AQC0647) [2] by Arnaud Quercy [2] using k-means clustering method with 10 color extraction parameters. Analysis includes color distribution, texture metrics, brightness/contrast measurements, and spatial pattern characterization. Analysis completed on 2026-02-04.

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]: 1984x2976 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 EA9218 37.5 orange goldenrod
2 E7B626 13.8 yellow-orange orange
3 20524C 10.7 green darkslategray
4 F1CB36 9.4 yellow-orange gold
5 15110C 8.7 black black
6 549995 6.9 green cadetblue
7 3C6462 6.1 green darkslategrey
8 D2D4C8 3.1 yellow-green lightgray
9 4E3C24 2.0 orange dark brown
10 A6A352 1.7 yellow ochre

Color Families:

Family %
orange 39.5
green 23.7
yellow-orange 23.2
black 8.7
yellow-green 3.1
yellow 1.7

Texture Analysis

Metric Value
Global Roughness 0.218
Mean Local Roughness 0.024
Roughness Uniformity 0.032
Edge Density 0.077
Mean Gradient Magnitude 0.184
Gradient Variance 0.099
Gradient Smoothness 0.0
Directional Coherence 0.02
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.644
Spatial Variation 0.164
Texture Consistency 0.463

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.537
Brightness Variance 0.218
Brightness Uniformity 0.593
Brightness Skewness -0.861
Brightness Entropy 7.17
Rms Contrast 0.218
Michelson Contrast 1.0
Weber Contrast 0.708
Mean Local Contrast 0.025
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.718
Shadow Percentage 23.995
Midtone Percentage 47.533
Highlight Percentage 28.471
Shadow Clipping 0.045
Highlight Clipping 0.015
Tonal Balance 0.0
Fine Contrast 0.013
Medium Contrast 0.033
Coarse Contrast 0.049
Multiscale Contrast Ratio 0.263
Edge Contrast 0.184
Contrast Clustering 0.537

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.751
Color Clustering 0.628
Color Transition Smoothness 0.531
Transition Uniformity 0.325
Sharp Transition Ratio 0.1
Transition Directionality 0.028
Mean Saturation 0.711
Saturation Variance 0.053
Low Saturation Ratio 0.065
Medium Saturation Ratio 0.305
High Saturation Ratio 0.629
Saturation Clustering 0.997
Hue Concentration 0.584
Complementary Balance 0.006
Analogous Dominance 0.748
Temperature Bias 0.487

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). A Major - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0647.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/a-major-research-on-harmony_77u.html

[3] Quercy, A. (2025). Computational Image Analysis Standard - MMIDS-CMP-2025 https://multimodal.institute/en/publications/2025/10/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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