Basic Models of Descriptive Image Analysis

This paper is devoted to the basic models of descriptive image analysis, which is the leading branch of the modern mathematical theory of image analysis and recognition.Descriptive analysis provides for the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation that is transformed from the original form into a form that is convenient for recognition (i.e., into a model), and models for converting data representations. Image analysis processes are considered as sequences of transformations that are implemented in the phase space and provide the construction of phase states of the image, which form a phase trajectory of the image translation from the original view to the model.Two types of image analysis models are considered: 1) models that reflect the general properties of the process of image recognition and analysis – the setting of the task, the mathematical and heuristic methods used, and the algorithmic content of the process: a) a model based on a reverse algebraic closure; b) a model based on the equivalence property of images; c) a model based on multiple image models and multiple classifiers; 2) models that characterize the architecture and structure of the recognition process: a) a multilevel model for combining algorithms and source data in image recognition; b) an information structure for generating descriptive algorithmic schemes for image recognition.A brief description, a comparative analysis of the relationships and specifics of these models are given. Directions for further research are discussed.