Open Access

A Discrete Model for Color Naming

EURASIP Journal on Advances in Signal Processing20062007:029125

https://doi.org/10.1155/2007/29125

Received: 3 January 2006

Accepted: 29 June 2006

Published: 15 October 2006

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.

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Authors’ Affiliations

(1)
Department of Information Engineering, Faculty of Telecommunications, University of Siena
(2)
Systems and Information Sciences Laboratory, UMR CNRS 6168

References

  1. Belpaeme T: Simulating the formation of color categories. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '01), August 2001, Seattle, Wash, USA 393-398.Google Scholar
  2. Belpaeme T: Reaching coherent color categories through communication. Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC '01), October 2001, Amsterdam, The Netherlands 41-48.Google Scholar
  3. Hardin CL: Basic color terms and basic color categories. In Color Vision: Perspectives from Different Disciplines. Walter de Gruyter, Berlin, Germany; 1998. chapter 11Google Scholar
  4. Berlin B, Kay P: Basic Color Terms: Their Universality and Evolution. University of California Press, Berkeley, Calif, USA; 1969.Google Scholar
  5. Sturges J, Whitfield TWA: Locating basic colours in the munsell space. Color Research and Application 1995, 20: 364-376. 10.1002/col.5080200605View ArticleGoogle Scholar
  6. Sturges J, Whitfield TWA: Salient features of Munsell colour space as a function of monolexemic naming and response latencies. Vision Research 1997,37(3):307-313. 10.1016/S0042-6989(96)00170-8View ArticleGoogle Scholar
  7. Lammens JM: A computational model of color perception and color naming, Ph.D. thesis. State University of New York, Buffalo, NY, USA; June 1994.Google Scholar
  8. Bleys J: The cultural propagation of color categories: insights from computational modeling, Ph.D. thesis. Vrjie University Brussel, Brussels, Belgium; 2004.Google Scholar
  9. Mojsilović A: A computational model for color naming and describing color composition of images. IEEE Transactions on Image Processing 2005,14(5):690-699.View ArticleGoogle Scholar
  10. Boynton RM, Olson CX: Locating basic colors in the OSA space. Color Research and Application 1987,12(2):94-105. 10.1002/col.5080120209View ArticleGoogle Scholar
  11. Wyszecki G, Stiles WS: Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley & Sons, New York, NY, USA; 1982.Google Scholar
  12. Kelly K, Judd D: The ISCC-NBS color names dictionary and the universal color language (the ISCC-NBS method of designating colors and a dictionary for color names). In Tech. Rep. Circular 553. National Bureau of Standards, Washington, DC, USA; November 1955.Google Scholar
  13. Delaunay B: Sur la sphère vide. Bulletin of the Academy of Sciences of the USSR, Classe des Sciences Mathèmatiques et Naturelles 1934,7(6):793-800.Google Scholar
  14. Lorensen WE, Cline HE: Marching cubes: a high resolution 3D surface construction algorithm. Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '87), 1987, New York, NY, USA 21: 163-169.View ArticleGoogle Scholar
  15. Cao D, Pokorny J, Smith VC: Associating color appearance with the cone chromaticity space. Vision Research 2005,45(15):1929-1934. 10.1016/j.visres.2005.01.033View ArticleGoogle Scholar

Copyright

© Menegaz et al. 2007