Natural Image processing and understanding encompasses hundreds or even thousands of different algorithms.
Each algorithm has a certain peak performance for a particular set of input features and configurations of
the objects/regions of the input image (environment). To obtain the best possible result of processing, we
propose an algorithm selection approach that permits to always use the most appropriate algorithm for the
given input image. This is obtained by at first selecting an algorithm based on low level features such as color
intensity, histograms, spectral coefficients. The resulting high level image description is then analyzed for logical
inconsistencies (contradictions) that are then used to refine the selection of the processing elements. The feedback
created from the contradiction information is executed by a Bayesian Network that integrates both the features
and a higher level information selection processes. The selection stops when the high level inconsistencies are all
resolved or no more different algorithms can be selected.