Fuzzy Color Spaces: A Conceptual Approach to Color Vision

In this paper, we introduce formal definitions of the concepts of fuzzy color and fuzzy color space. First, we formalize the notion of fuzzy color for representing the correspondence between computational representation of colors and perceptual color categories identified by a color name. Second, we propose a methodology for learning fuzzy colors based on the paradigm of conceptual spaces, where prototypes are used for each category to be learnt. Since the conceptual space approach yields crisp categorizations, we introduce a novel methodology for defining fuzzy boundaries of color categories on the basis of a Voronoi tessellation of a color space. Finally, we also formalize the notion of fuzzy color space as the collection of fuzzy colors corresponding to the color categories employed in a certain context/application and/or for a specific user. Different typologies of fuzzy color spaces are proposed in order to be consistent with the nature of the categories we want to model. Our approach is illustrated by defining fuzzy color spaces using RGB with the Euclidean distance. Examples based on the well-known ISCC-NBS color naming system are presented, as well as others based on collections of color names and prototypes provided by users. The proposal is evaluated and compared with the most used approaches for color modeling. Additionally, a website located at http://www.jfcssoftware.com including all experimentation data, software implementing our models, and additional materials is available to researchers in color modeling.