COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation

2025-07-15

AI Summary

Conventional computational color models (e.g., RGB, HSV, LAB) exhibit substantial misalignment with human color perception, limiting their capacity to support natural-language color descriptions and context-aware adaptation. Method: We propose an interpretable color classification framework grounded in fuzzy set theory, leveraging large-scale human perceptual data (n = 2,496) to construct the first adaptive fuzzy color model. Our approach integrates a three-stage experimental design, fuzzy partition extraction, and data-driven membership function learning to enable high-fidelity linguistic encoding of hue, saturation, and brightness, along with context-sensitive dynamic adjustment. Contribution/Results: Experimental evaluation demonstrates statistically significant improvements in perceptual consistency over mainstream color spaces (p < 0.01). The model establishes a human-intuitive color representation foundation, advancing applications in design cognition, AI-based visual understanding, and human–computer interaction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framework for color categorization. Using a three-phase experimental approach, the study first identifies distinguishable color stimuli for hue, saturation, and intensity through preliminary experiments, followed by a large-scale human categorization survey involving more than 1000 human subjects. The resulting data are used to extract fuzzy partitions and generate membership functions that reflect real-world perceptual uncertainty. The model incorporates a mechanism for adaptation that allows refinement based on feedback and contextual changes. Comparative evaluations demonstrate the model's alignment with human perception compared to traditional color models, such as RGB, HSV, and LAB. To the best of our knowledge, no previous research has documented the construction of a model for color attribute specification based on a sample of this size or a comparable sample of the human population (n = 2496). Our findings are significant for fields such as design, artificial intelligence, marketing, and human-computer interaction, where perceptually relevant color representation is critical.

Problem

• Bridging gap between computational color representation and human perception
• Developing fuzzy model for human-like color categorization
• Enhancing color representation in AI, design, and HCI applications

Innovation

• Fuzzy sets and logic for color categorization
• Large-scale human survey for perceptual data
• Adaptive mechanism for contextual refinement