This article presents a generic computational framework necessary for the solution of image understanding problem. The hierarchical networks, represented dually as discrete and continuous structures, are data and algorithms at the same time within that framework. Such structures are able to perform both graph and diagrammatic operations being the basis of intelligence. Dual representation provides natural transformation of continuous image information into primary discrete structures, making the image available for analysis. The computational methods create further higher-level derivative structures, which can run top-bottom algorithms and play role of the context or measurement device, giving the ability to analyze. Symbols naturally emerge in such structures and symbolic operations work there in the combination with the new proposed simplified methods of computational intelligence. That makes images and scenes self-describing, and provides flexible ways of resolving uncertainty. Classification of images truly invariant to any transformation could be done via matching not their primary, but their derivative structures. The ability of image applications to resolve ambiguity and uncertainty in the real images requires tight integration of low-level image processing with high-level knowledge-based reasoning, which is the solution of the image understanding the problem. The proposed architecture does not require supercomputers, opening new ways for image applications.