Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Discrete Model for Color Naming | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 29125 10 pages doi 2007 29125 Research Article A Discrete Model for Color Naming G. Menegaz 1 A. Le Troter 2 J. Sequeira 2 and J. M. Boi2 1 Department of Information Engineering Faculty of Telecommunications University of Siena Siena 53100 Rome Italy 2 Systems and Information Sciences Laboratory UMR CNRS 6168 13397 Marseille France Received 3 January 2006 Revised 2 June 2006 Accepted 29 June 2006 Recommended by Maria Concetta Morrone 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 .
