Descriptive Models of Information Transformation Processes in Image Analysis

• Published in: Pattern recognition and image analysis Vol. 31; no. 3; pp. 402 - 420
• Main Authors: Gurevich, I. B., Yashina, V. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Analysis; Computer Science; Decision analysis; Heuristic methods; Image analysis; Image Processing and Computer Vision; Mathematical analysis; Mathematical Theory of Images and Signals Representing; Object recognition; Pattern Recognition; Processing; Recognition; Representations; Sequences; Transformations (mathematics); Understanding; mathematical structures; formal systems; image analysis; image models; image recognition algorithms; image representations; information structures; automated extraction of information from images; mathematical theory of image analysis; descriptive algorithmic schemes; models of image analysis procedures; descriptive image analysis; image recognition
• ISSN: 1054-6618; 1555-6212
• DOI: 10.1134/S105466182103010X

— This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of image analysis and recognition. The fundamental problem that the subject of the article addresses is automated extraction of information from images necessary for making intelligent decisions. Descriptive analysis envisages the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation, which is converted from the original form to one convenient for recognition (i.e., to a model), and data representation conversion models. The image analysis processes are considered to be sequences of transformations implemented in the phase space and providing the construction of phase states of the image, which together form the phase trajectory of image transfer from the original form to the model. The study of the image formalization space leads to formalization of the concepts of representation/image model, as well as to construction of models of the image recognition and analysis processes and formulation of mathematical statements of image recognition and analysis problems. Two types of image analysis models are considered: (1) models reflecting the methodology and mathematical foundations of image recognition and analysis—the problem statement, used mathematical and heuristic methods, algorithmic content of the process: (a) a model based on reverse algebraic closure, (b) a model based on the property of equivalence of images of the same class, (c) a model based on multiple image models and multiple classifiers; (2) models characterizing the architecture and structure of the recognition process: (a) a multilevel model for combining algorithms and initial data for image recognition, (b) information structure for generating descriptive algorithmic image recognition schemes. A brief description of the above models is given. A comparative analysis of the relationships and specifics of the models is carried out. Directions for further research are discussed.