COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation

TL;DR

The paper presents COLIBRI, a fuzzy color model based on extensive human perception data, which improves computational color representation by aligning it more closely with how humans perceive colors.

Contribution

It introduces a novel fuzzy color model derived from large-scale human categorization data, enhancing the alignment of computational color models with human perception.

Findings

Model aligns better with human perception than RGB, HSV, LAB

Uses large-scale survey data from over 2,400 subjects

Provides adaptive mechanism for contextual color interpretation

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.