  In this work, a multiple-expert binarization framework for multispectral
images is proposed. The framework is based on a constrained subspace selection
limited to the spectral bands combined with state-of-the-art gray-level
binarization methods. The framework uses a binarization wrapper to enhance the
performance of the gray-level binarization. Nonlinear preprocessing of the
individual spectral bands is used to enhance the textual information. An
evolutionary optimizer is considered to obtain the optimal and some suboptimal
3-band subspaces from which an ensemble of experts is then formed. The
framework is applied to a ground truth multispectral dataset with promising
results. In addition, a generalization to the cross-validation approach is
developed that not only evaluates generalizability of the framework, it also
provides a practical instance of the selected experts that could be then
applied to unseen inputs despite the small size of the given ground truth
dataset.
