AQC0886

Nanopublication — Computational Image Analysis - AQC0886

Ab Major - Research on Harmony - Variations 12

Claim 1: Computational Image Analysis - AQC0886

Computational image analysis [3] of artwork Ab Major [1] - Research on Harmony - Variations 12 (AQC0886) [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 2025-12-11.

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]: 1877x2815 pixels. Analysis date: 2025-12-11.

Color Analysis

Rank Color Hex % Family Name
1 70879F 21.8 blue-violet grayish purple
2 A1A2A7 12.0 gray steel gray
3 694D3A 11.4 orange dark brown
4 696B7A 10.7 violet dusty mauve
5 F3CC9C 10.6 yellow-orange navajowhite
6 DBAC83 10.2 orange burlywood
7 80A8C2 9.3 blue steel gray
8 E84A1A 5.6 red-orange orangered
9 B5C0CB 5.1 blue silver
10 2E1911 3.4 orange very dark gray
11 FEF7B4 0.3 yellow moccasin [Accent]
12 AB7899 0.3 red-violet dusty mauve [Accent]
13 976972 0.3 red gray [Accent]

Color Families:

Family %
orange 25.0
blue-violet 21.8
blue 14.4
gray 12.0
violet 10.7
yellow-orange 10.6
red-orange 5.6
yellow 0.3
red-violet 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
FEF7B4 yellow moccasin 34.0
AB7899 red-violet dusty mauve 26.9
976972 red gray 20.1

Texture Analysis

Metric Value
Global Roughness 0.176
Mean Local Roughness 0.04
Roughness Uniformity 0.032
Edge Density 0.223
Mean Gradient Magnitude 0.324
Gradient Variance 0.123
Gradient Smoothness 0.0
Directional Coherence 0.005
Pattern Complexity 0.115
Pattern Repetition 1.0
Detail Frequency Ratio 0.649
Spatial Variation 0.083
Texture Consistency 0.763

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.558
Brightness Variance 0.176
Brightness Uniformity 0.684
Brightness Skewness -0.338
Brightness Entropy 7.469
Rms Contrast 0.176
Michelson Contrast 1.0
Weber Contrast 0.575
Mean Local Contrast 0.044
Contrast Uniformity 0.248
Dynamic Range 1.0
Effective Dynamic Range 0.557
Shadow Percentage 9.729
Midtone Percentage 60.293
Highlight Percentage 29.978
Shadow Clipping 0.006
Highlight Clipping 0.004
Tonal Balance 0.181
Fine Contrast 0.022
Medium Contrast 0.054
Coarse Contrast 0.078
Multiscale Contrast Ratio 0.276
Edge Contrast 0.324
Contrast Clustering 0.237

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.697
Color Clustering 0.4
Color Transition Smoothness 0.179
Transition Uniformity 0.179
Sharp Transition Ratio 0.1
Transition Directionality 0.006
Mean Saturation 0.344
Saturation Variance 0.045
Low Saturation Ratio 0.393
Medium Saturation Ratio 0.531
High Saturation Ratio 0.077
Saturation Clustering 0.998
Hue Concentration 0.108
Complementary Balance 0.293
Analogous Dominance 0.534
Temperature Bias 0.114

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 (2025). Ab Major - Research on Harmony - Variations 12 — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0886.html
https://arnaudquercy.art/fr/catalogue-raisonne/AQC0886.html

[2] Quercy, A. (2025). Ab Major - Research on Harmony - Variations 12 - Gallery. https://artquamanima.com/en/artworks/2025/11/ab-major-research-on-harmony-variations-12_i5a.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)

381b389694e61db75505c6176af92ffe057110133548ae2b2a82103deeeb1e0c