  The pixel's classification of images obtained from random heterogeneous
materials is a relevant step to compute their physical properties, like
Effective Transport Coefficients (ETC), during a characterization process as
stochastic reconstruction. A bad classification will impact on the computed
properties; however, the literature on the topic discusses mainly the
correlation functions or the properties formulae, giving little or no attention
to the classification; authors mention either the use of a threshold or, in few
cases, the use of Otsu's method. This paper presents a classification approach
based on Support Vector Machines (SVM) and a comparison with the Otsu's-based
approach, based on accuracy and precision. The data used for the SVM training
are the key for a better classification; these data are the grayscale value,
the magnitude and direction of pixels gradient. For instance, in the case
study, the accuracy of the pixel's classification is 77.6% for the SVM method
and 40.9% for Otsu's method. Finally, a discussion about the impact on the
correlation functions is presented in order to show the benefits of the
proposal.
