AQC0583

Nanopublication — Computational Image Analysis - AQC0583

by Arnaud Quercy · C minor - Research on Harmony · 2024

Claim 1: Computational Image Analysis - AQC0583

K-means clustering analysis [3] (10 colors) performed on artwork C minor - Research [1] on Harmony (AQC0583) [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]: 2425x3074 pixels. Analysis date: 2026-02-04.

Color Analysis

Rank Color Hex % Family Name
1 CB8E8B 14.8 red-orange rosybrown
2 280C47 13.2 violet very dark purple
3 30100C 12.7 red-orange very dark red
4 B97F7B 11.8 red-orange palevioletred
5 AC2A09 9.3 red-orange firebrick
6 E3B8AE 9.1 red-orange lightpink
7 CCA898 8.9 orange tan
8 9F6763 8.6 red-orange indianred
9 AB987A 6.4 yellow-orange ochre
10 84300E 5.2 orange russet
11 54285E 0.3 red-violet dusty mauve [Accent]
12 5E3138 0.3 red dark brown [Accent]

Color Families:

Family %
red-orange 66.4
orange 14.0
violet 13.2
yellow-orange 6.4
red-violet 0.3
red 0.3

Accent Colors:

Hex Family Name Chroma
54285E red-violet dusty mauve 38.4
5E3138 red dark brown 21.4

Texture Analysis

Metric Value
Global Roughness 0.241
Mean Local Roughness 0.017
Roughness Uniformity 0.019
Edge Density 0.064
Mean Gradient Magnitude 0.126
Gradient Variance 0.033
Gradient Smoothness 0.0
Directional Coherence 0.051
Pattern Complexity 0.138
Pattern Repetition 1.0
Detail Frequency Ratio 0.648
Spatial Variation 0.147
Texture Consistency 0.526

Brightness & Contrast Analysis

Metric Value
Mean Brightness 0.441
Brightness Variance 0.241
Brightness Uniformity 0.454
Brightness Skewness -0.294
Brightness Entropy 7.22
Rms Contrast 0.241
Michelson Contrast 1.0
Weber Contrast 0.87
Mean Local Contrast 0.018
Contrast Uniformity 0.0
Dynamic Range 1.0
Effective Dynamic Range 0.682
Shadow Percentage 38.664
Midtone Percentage 42.984
Highlight Percentage 18.352
Shadow Clipping 0.001
Highlight Clipping 0.0
Tonal Balance 0.0
Fine Contrast 0.01
Medium Contrast 0.023
Coarse Contrast None
Multiscale Contrast Ratio 1.0
Edge Contrast 0.126
Contrast Clustering 0.474

Spatial Distribution Analysis

Metric Value
Spatial Coherence 0.75
Color Clustering 0.694
Color Transition Smoothness 0.674
Transition Uniformity 0.781
Sharp Transition Ratio 0.1
Transition Directionality 0.058
Mean Saturation 0.52
Saturation Variance 0.077
Low Saturation Ratio 0.285
Medium Saturation Ratio 0.355
High Saturation Ratio 0.36
Saturation Clustering 0.999
Hue Concentration 0.834
Complementary Balance 0.0
Analogous Dominance 0.865
Temperature Bias 0.867

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). C minor - Research on Harmony — Catalog raisonné. https://arnaudquercy.art/en/catalogue-raisonne/AQC0583.html

[2] Quercy, A. (2025). Untitled - Gallery. https://artquamanima.com/en/artworks/2024/01/c-minor-research-on-harmony_6iy.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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