COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
This work addresses the problem of perceptually relevant color representation for fields such as design, AI, marketing, and human-computer interaction, though it is incremental as it builds on existing fuzzy logic approaches.
The paper tackles the challenge of computers imitating human color perception by introducing the COLIBRI fuzzy model, which uses fuzzy sets and logic based on a large-scale human categorization survey (n=2496) to align with human perception, demonstrating improved alignment compared to traditional color models like RGB, HSV, and LAB.
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.