Linguistic color image segmentation using a hierarchical Bayesian approach

In this work, we combine Bayesian techniques with a color categorization model, which leads to a method for the linguistic segmentation of color images. The categorization model considers the 11 universal color categories proposed by Berlin and Kay [ Basic Color Terms: Their Universality and Evolution. Berkeley: University of California; 1969]. The likelihood for each category is represented by a linear combination of quadratic splines, and as a result, each voxel in the color space L*u*v* is described as a vector of probabilities, whose components express the degree to which the voxel belongs to a given color category. This gives rise to a probabilistic dictionary which is used for the segmentation, in which prior spatial granularity constraints are incorporated via an entropy‐controlled quadratic Markov measure field (ECQMMF) model, as proposed by Rivera et al. [IEEE Trans Image Process 2007;16:3047–3057]. We give a generalization of ECQMMF that allows one to consider the perceptual interactions between the basic colors that were experimentally established by Boynton and Olson [Color Res Appl 1987;12:94–105]. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 299–309, 2009