1. A method, comprising: chromatic classifying pixels of a digital image by, transforming a chromatic domain of definition of pixels from a three-dimensional space into a respective two-dimensional normalized space; defining a reference model of classification by analyzing a plurality of images of distinct types of subjects, generating distinct collections of samples with respective coordinates in the normalized two-dimensional space for each of a number of chromatic classes corresponding to real subjects; and evaluating for each image pixel to be classified a membership class and a membership level within the membership class based on the reference model, wherein said reference model is defined by a parametric membership function continuous on said normalized two-dimensional space for each of said chromatic classes, parameters of which are determined by processing the collection of two-dimensional samples of each of said chromatic classes, said chromatic classifying, transforming, defining and evaluating are performed by one or more configured processing devices, and wherein defining the reference model comprises calculating a mean position of clusters of samples on the normalized two-dimensional space, a variance in respect to a principal direction and to a direction orthogonal to said principal direction and a slope angle of said principal direction using a principal component analysis technique, thus obtaining an elliptical Gaussian model for each of said chromatic classes on said normalized two-dimensional space.