This paper is an attempt to develop a coherent framework for understanding, modeling, and computing color categories. The main assumption is that the structure of color category systems originates from the statistical structure of the perceived color environment. This environment can be modeled as color statistics of natural images in some perceptual and approximately uniform color space (e.g., the CIELUV color space). The process of color categorization can be modeled as the grouping of the color statistics by clustering algorithms (e.g., K-means). The proposed computational model enable to predict the location, order, and number of color categories. The model is examined on the basis of K-means clustering analysis of statistics of 630 natural images in the CIELUV color space. In general, the predictions are consistent with Berlin and Kai, and Boynton and Oslon2 data.
|Name||Proceedings of SPIE|
|Conference||conference; Human vision and Electronic Imaging V; 2000-01-24; 2000-01-27|
|Period||24/01/00 → 27/01/00|
|Other||Human vision and Electronic Imaging V|