EURASIP Journal on Advances in Signal Processing 
Volume 2007 (2007), Article ID 29125, 10 pages
doi:10.1155/2007/29125
Research Article

A Discrete Model for Color Naming

G. Menegaz,1 A. Le Troter,2 J. Sequeira,2 and J. M. Boi2

 1Department of Information Engineering, Faculty of Telecommunications, University of Siena, Siena 53100, Rome, Italy
2Systems and Information Sciences Laboratory, UMR CNRS 6168, Marseille 13397, France
Received 3 January 2006; Revised 2 June 2006; Accepted 29 June 2006

Recommended by Maria Concetta Morrone

Abstract

The ability to associate labels to colors is very natural for human beings. Though, this apparently simple task hides very complex and still unsolved problems, spreading over many different disciplines ranging from neurophysiology to psychology and imaging. In this paper, we propose a discrete model for computational color categorization and naming. Starting from the 424 color specimens of the OSA-UCS set, we propose a fuzzy partitioning of the color space. Each of the 11 basic color categories identified by Berlin and Kay is modeled as a fuzzy set whose membership function is implicitly defined by fitting the model to the results of an ad hoc psychophysical experiment (Experiment 1). Each OSA-UCS sample is represented by a feature vector whose components are the memberships to the different categories. The discrete model consists of a three-dimensional Delaunay triangulation of the CIELAB color space which associates each OSA-UCS sample to a vertex of a 3D tetrahedron. Linear interpolation is used to estimate the membership values of any other point in the color space. Model validation is performed both directly, through the comparison of the predicted membership values to the subjective counterparts, as evaluated via another psychophysical test (Experiment 2), and indirectly, through the investigation of its exploitability for image segmentation. The model has proved to be successful in both cases, providing an estimation of the membership values in good agreement with the subjective measures as well as a semantically meaningful color-based segmentation map.