# Color-Emotion Associations in Art: Fuzzy Approach

arXiv: https://arxiv.org/abs/2311.18518 · HTML: https://mlsanity.com/paper/2311.18518/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 78 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Stimuli-Aware Visual Emotion Analysis](https://mlsanity.com/paper/2109.01812/) (2021)
- [Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children](https://mlsanity.com/paper/2309.00138/) (2023)
- [Towards a Universal Understanding of Color Harmony: Fuzzy Approach](https://mlsanity.com/paper/2310.00791/) (2023)

## Built on by (most cited first, 8 of 8)

- [COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation](https://mlsanity.com/paper/2507.11488/) (2025)
- [Comparative Analysis Of Color Models For Human Perception And Visual Color Difference](https://mlsanity.com/paper/2406.19520/) (2024)
- [Color Models in Image Processing: A Review and Experimental Comparison](https://mlsanity.com/paper/2510.00584/) (2025)
- [Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis](https://mlsanity.com/paper/2408.01838/) (2024)
- [Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation](https://mlsanity.com/paper/2509.10058/) (2025)
- [Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems](https://mlsanity.com/paper/2404.13778/) (2024)
- [Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion](https://mlsanity.com/paper/2603.18466/) (2026)
- [Aesthetic Preference Prediction in Interior Design: Fuzzy Approach](https://mlsanity.com/paper/2401.17710/) (2024)

## Abstract (arXiv)

Art objects can evoke certain emotions. Color is a fundamental element of visual art and plays a significant role in how art is perceived. This paper introduces a novel approach to classifying emotions in art using Fuzzy Sets. We employ a fuzzy approach because it aligns well with human judgments' imprecise and subjective nature. Extensive fuzzy colors (n=120) and a broad emotional spectrum (n=10) allow for a more human-consistent and context-aware exploration of emotions inherent in paintings. First, we introduce the fuzzy color representation model. Then, at the fuzzification stage, we process the Wiki Art Dataset of paintings tagged with emotions, extracting fuzzy dominant colors linked to specific emotions. This results in fuzzy color distributions for ten emotions. Finally, we convert them back to a crisp domain, obtaining a knowledge base of color-emotion associations in primary colors. Our findings reveal strong associations between specific emotions and colors; for instance, gratitude strongly correlates with green, brown, and orange. Other noteworthy associations include brown and anger, orange with shame, yellow with happiness, and gray with fear. Using these associations and Jaccard similarity, we can find the emotions in the arbitrary untagged image. We conducted a 2AFC experiment involving human subjects to evaluate the proposed method. The average hit rate of 0.77 indicates a significant correlation between the method's predictions and human perception. The proposed method is simple to adapt to art painting retrieval systems. The study contributes to the theoretical understanding of color-emotion associations in art, offering valuable insights for various practical applications besides art, like marketing, design, and psychology.