Information criterion for constructing the hierarchical structural representations of images

The aim of investigation consists in development of a formal image representation, in whose framework the most relevant information can be extracted from images. Constructing the models of images is considered as a task of inductive inference. The conventional criterions for choosing the best model are based on the Bayesian rule. However there is one classical problem of defining the a priori probabilities of models. The generally adopted approach for overcoming this difficulty is to use the Minimum Description Length (MDL) principle. In the task of interpretation of visual scenes the a priori probabilities of realizations of images are assigned by their representation language. In our work we study the hierarchical structural descriptions of images. A problem of selection of alphabet of structural elements is addressed. Such the commonly used structural elements as the straight lines, angles, arcs, and others are considered, and their usage is grounded on the base of the amount of information contained in them. The composite structural elements can be formed within the framework of hierarchical representations. The grouping rules are generally based on some similarities in the elements. Hence the descriptions of these elements contain the positive mutual information. Such the approach permits to proof the usage of these structural elements, to choose rationally their types, and to elaborate a rigorous criterion of grouping. The results of research implemented in the form of computer programs showed the appropriateness of this approach.