Color categorization models for color image segmentation

Abstract

In 1969, Brent Berlin and Paul Kay presented a classic study of color namingwhere experimentally demonstrated that all languages share a universal color assignment system of 11 basic color categories. Based on this work, new color categorization models have appeared in order to confirm this theory. Some of these models assign one category to each color in a certain color space, while other models assign a degree of membership to each category. The degree of membership can be interpreted as the probability of a color to belong to a color category. In the first part of this work we review some color categorization models: discrete and fuzzy based models. Then, we pay special attention to a recent color categorization model that provides a probabilistic partition of a color space, which was proposed by Alarcon and Marroquin in 2009. The proposal combines the color categorization model with a probabilistic segmentation algorithm and also generalizes the probabilistic segmentation algorithm so that one can include interaction between categories. We present some experiments of color image segmentation and applications of color image segmentation to image and video recolourization and tracking.