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Descriptive Image Analysis

  • SCIENTIFIC SCHOOLS OF THE FEDERAL RESEARCH CENTER “COMPUTER SCIENCE AND CONTROL” OF THE RUSSIAN ACADEMY OF SCIENCES, MOSCOW, THE RUSSIAN FEDERATION
  • I.B. Gurevich’s Scientific School
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

An overview of the main methods, models, and results of Descriptive Image Analysis is given. Descriptive Image Analysis is a logically organized set of descriptive methods and models designed for image analysis and evaluation. The state of the art and trends in the development of Descriptive Image Analysis are determined by the methods, models, and results of the Descriptive Approach to image analysis and understanding. As the methods and apparatus of the Descriptive Approach to the analysis and understanding of images were developed and refined, its interpretation was proposed, defined as Descriptive Image Analysis. The main goal of Descriptive Image Analysis is to structure and standardize the various methods, processes, and concepts used in image analysis and recognition. Descriptive Image Analysis solves the fundamental problems of formalizing and systematizing methods and forms of information representation in image analysis, recognition, and understanding problems, in particular, associated with automating the extraction of information from images to make intelligent decisions (diagnosis, prediction, detection, assessment, and identification patterns of objects, events and processes). Descriptive Image Analysis makes it possible to solve both problems related to constructing formal descriptions of images as recognition objects and problems of synthesizing procedures for recognizing and understanding images. It is suggested that the processes of analysis and evaluation of information represented in the form of images (problem solution trajectories) can generally be considered a sequence/combination of transformations and calculations of a set of intermediate and final (determining the solution) estimates. These transformations are defined by equivalence classes of images and their representations. The latter are defined descriptively, i.e., using a basic set of prototypes and corresponding generating transformations that are functionally complete with respect to the equivalence class of admissible transformations. As part of Descriptive Image Analysis, the following main results were obtained: (1) new mathematical objects were introduced and studied: image formalization space, descriptive image algebras, descriptive algorithmic schemes; (2) descriptive image analysis models have been defined and studied: image models, image transformation models, models for generating descriptive algorithmic schemes; (3) linguistic and knowledge-oriented tools have been developed to support the automation of image analysis; (4) a number of automated software systems have been developed and axioms for Descriptive Image Analysis proposed. A general description of the provisions of Descriptive Image Analysis is presented, and the main results of research in the first two directions are discussed: new mathematical objects and image analysis models. A comprehensive bibliography on Descriptive Image Analysis is provided.

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  83. I. B. Gurevich and N. S. Polikarpova, “An efficient method of image matching based on fractal dimension,” Pattern Recognit. Image Anal. 8, 188–190 (1998).

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  85. I. B. Gurevich, O. Salvetti, and Yu. O. Trusova, “Fundamental concepts and elements of image analysis ontology,” Pattern Recognit. Image Anal. 19, 603–611 (2009). https://doi.org/10.1134/s1054661809040051

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  86. I. B. Gurevich and Yu. G. Smetanin, “On the equivalence of images in pattern recognition problems,” in Proc. 12th Scandinavian Conf. on Image Analysis, Ed. by I. Austvoll (Norwegian Society for Image Processing and Pattern Recognition, Bergen, 2001), pp. 679–685.

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  89. I. B. Gurevich, Yu. G. Smetanin, and Yu. I. Zhuravlev, “Mathematical models related to automation of ensuring safety at a subway platform problems,” Pattern Recognit. Image Anal. 9, 49–51 (1999).

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  90. I. B. Gurevich, Yu. G. Smetanin, and Yu. I. Zhuravlev, “Descriptive image algebras: Determination of the base structures,” Pattern Recognit. Image Anal. 9, 635–647 (1999).

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  92. I. B. Gurevich, Yu. G. Smetanin, and Yu. I. Zhuravlev, “On the development of an algebra of images and image analysis algorithms,” in Proc. 11th Scandinavian Conf. on Image Analysis (Kangerlussuaq, Greenland, Denmark, 1999), Vol. 1, pp. 479–485.

  93. I. Gurevich, Yu. Trusova, and V. Yashina, “Current trends in the algebraic image analysis: A survey,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, Vol. 8258 (Springer, Berlin, 2013), pp. 423–430. https://doi.org/10.1007/978-3-642-41822-8_53

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  94. I. B. Gurevich, V. A. Vlasova, I. A. Vorobjev, D. M. Murashov, and A. A. Trykova, “Development of theoretical foundations for information technology for diagnostic analysis of cell specimens images and implementation of algorithmic-software package supporting its application,” in Proc. Conf. Fundamental Sciences to Medicine (Slovo, Moscow, 2005), pp. 130–133.

  95. I. B. Gurevich, I. A. Vorobjev, I. S. Mekhedov, A. V. Nefyodov, A. A. Trykova, and D. V. Harazishvili, “Information technology for interactive morphological analysis and diagnostics of lymphatic system tumors,” in Proc. Conference Fundamental Sciences to Medicine (Slovo, Moscow, 2004), pp. 118–121.

  96. I. B. Gurevich and V. V. Yashina, “Operations of descriptive image algebras with one ring,” Pattern Recognit. Image Anal. 16, 298–328 (2006). https://doi.org/10.1134/s1054661806030035

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  97. I. B. Gurevich and V. V. Yashina, “Computer-aided image analysis based on the concepts of invariance and equivalence,” Pattern Recognit. Image Anal. 16, 564–589 (2006). https://doi.org/10.1134/s1054661806040031

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  98. I. B. Gurevich and V. V. Yashina, “Descriptive approach to image analysis: Image models,” Pattern Recognit. Image Anal. 18, 518–541 (2008). https://doi.org/10.1134/s1054661808040020

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  99. I. B. Gurevich and V. V. Yashina, “Descriptive approach to image analysis: Image formalization space,” Pattern Recognit. Image Anal. 22, 495–518 (2012). https://doi.org/10.1134/s1054661812040050

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  101. I. B. Gurevich, V. V. Yashina, S. V. Ablameyko, A. M. Nedzved, A. M. Ospanov, A. T. Tleubaev, A. A. Fedorov, and N. A. Fedoruk, “Development and experimental investigation of mathematical methods for automating the diagnostics and analysis of ophthalmological images,” Pattern Recognit. Image Anal. 28, 612–636 (2018). https://doi.org/10.1134/s1054661818040120

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  102. I. Gurevich and V. Yashina, “Descriptive image analysis. Foundations and descriptive image algebras,” Int. J. Pattern Recognit. Artif. Intell. 33, 1940018 (2019). https://doi.org/10.1142/s0218001419400184

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  104. I. B. Gurevich and V. V. Yashina, “Descriptive image analysis: Part II. Descriptive image models,” Pattern Recognit. Image Anal. 29, 598–612 (2019). https://doi.org/10.1134/s1054661819040035

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  105. I. Gurevich and V. Yashina, “Irrelation of mathematical and functional aspects of descriptive image algebras with one ring operations interpretability,” in Pattern Recognition and Information Processing. PRIP 2019, Ed. by S. Ablameyko, V. Krasnoproshin, and M. Lukashevich, Communications in Computer and Information Science, Vol. 1055 (Springer, Cham, 2019), pp. 74–85. https://doi.org/10.1007/978-3-030-35430-5_7

  106. I. B. Gurevich and V. V. Yashina, “Descriptive image analysis: Part III. Multilevel model for algorithms and initial data combining in pattern recognition,” Pattern Recognit. Image Anal. 30, 328–341 (2020). https://doi.org/10.1134/s1054661820030086

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  108. I. Gurevich and V. Yashina, “Basic models of descriptive image analysis,” in Pattern Recognition. ICPR International Workshops and Challenges, Ed. by A. Del Bimbo, Lecture Notes in Computer Science, Vol. 12665 (Springer, Cham, 2021), pp. 275–288. https://doi.org/10.1007/978-3-030-68821-9_26

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  109. I. B. Gurevich and V. V. Yashina, “Descriptive models of information transformation processes in image analysis,” Pattern Recognit. Image Anal. 31, 402–420 (2021). https://doi.org/10.1134/s105466182103010x

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  110. I. B. Gurevich and V. V. Yashina, “An example of satisfying the conditions of membership to a class of descriptive image algebras,” in Proc. 7th All-Russian Conf. with Participation of CIS on Methods and Means of Processing Complex Graphic Information (Nizhny Novgorod, 2003), pp. 51–52.

  111. I. B. Gurevich and V. V. Yashina, “On modeling descriptive image analysis procedures on a specialized Turing machine,” Pattern Recognit. Image Anal. 32, 469–476 (2022). https://doi.org/10.1134/s1054661822030142

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  112. I. B. Gurevich and V. Yashina, “Conditions of generating descriptive image algebras by a set of image processing operations,” in Progress in Pattern Recognition, Speech and Image Analysis: 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003, Ed. by A. Sanfeliu and J. Ruiz-Shulcloper, Lecture Notes in Computer Science, Vol. 2905 (Springer, Berlin, 2003), pp. 498–505. https://doi.org/10.1007/978-3-540-24586-5_61

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  115. I. Gurevich and V. Yashina, “Application of algebraic language in image analysis. Illustrative example,” in Proc. 7th Int. Conf. Pattern Recognition and Image Analysis: New Information Technologies (PRIA-7-2004) (S.-Peterb. Elektrotekh. Univ., St. Petersburg, 2004), Vol. 1, pp. 240–243.

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  117. I. Gurevich and V. Yashina, “Generating descriptive trees,” in Vision, Modeling, and Visualization 2005, Proc., Ed. by G. Greiner, J. Hornegger, H. Niemann, and M. Stamminger (Infix, Erlangen, Germany, 2005), pp. 367–374.

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  118. I. B. Gurevich, I. A. Zhernova, and Yu. G. Smetanin, “Basic notions of invariant image processing,” in Proc. 6th All-Russian Conf. with Participation of CIS on Methods and Means of Processing Complex Graphic Information (Nizhny Novgorod, 2001), pp. 82–83.

  119. I. B. Gurevich and Yu. I. Zhuravlev, “An image algebra accepting image models and image transforms,” in Proc. 7th Int. Workshop Vision, Modeling, and Visualization 2002 (VMV2002), Erlangen, Germany, 2002, Ed. by G. Greiner, H. Niemann, T. Ertl, B. Girod, and H.-P. Seidel (IOS Press, Berlin, 2002), pp. 21–26.

  120. I. B. Gurevich, Yu. I. Zhuravlev, N. V. Klimova, and Yu. G. Smetanin, “Basic descriptive image algebras,” in Proc. 5th Int. Conf. Pattern Recognition and Image Analysis: New Information Technologies (PRIA-5-2000) (Samara, 2000), Vol. 2, pp. 260–264.

  121. I. B. Gurevich, A. A. Myagkov, Yu. O. Trusova, V. V. Yashina, and Yu. I. Zhuravlev, “On basic problems of image recognition in neurosciences and heuristic methods for their solution,” Pattern Recognit. Image Anal. 25, 132–160 (2015). https://doi.org/10.1134/s105466181501006x

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  122. I. B. Gurevich, Yu. I. Zhuravlev, D. M. Murashov, Yu. G. Smetanin, and A. V. Khilkov, “Program system for image analysis and understanding Black Square 1.0,” in Mathematical Techniques of Pattern Recognition: Proc. 9th All-Russian Conf. (Vychislitel’nyi Tsentr Ross. Akad. Nauk, Moscow, 1999), pp. 169–172.

  123. I. B. Gurevich, Yu. I. Zhuravlev, D. M. Murashov, Yu. G. Smetanin, and A. V. Khilkov, “A system for automation of scientific research on image analysis and understanding on the base of knowledge collection and use. Part 1,” Avtometriya, No. 6, 23–50 (1999).

  124. I. B. Gurevich, Yu. I. Zhuravlev, D. M. Murashov, Yu. G. Smetanin, and A. V. Khilkov, “Knowledge-based system for automatization of scientific research in image analysis and understanding. Part I,” Optoelectron. Instrum. Data Process., No. 6, 18–36 (1999).

  125. I. B. Gurevich, Yu. I. Zhuravlev, V. I. Robotishin, and Yu. G. Smetanin, “A synthesis of image representation for pattern recognition based on disjunctions of the threshold function,” Pattern Recognit. Image Anal. 8, 14–24 (1998).

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  128. I. B. Gurevich, Yu. I. Zhuravlev, and Yu. G. Smetanin, “On mathematical models connected with the automation of security maintaining on undeground platforms,” in Proc. 4th All-Russian Conf. with participation of CIS Pattern Recognition and Image Analysis: New Information Technologies (Inst. Avtom. i Elektrometrii Sib. Otd. Ross. Akad. Nauk, Novosibirsk, 1998), Vol. 1, pp. 79–82.

  129. I. B. Gurevich, Yu. I. Zhuravlev, and Yu. G. Smetanin, “Image algebras: Investigations and applied problems,” in Collection of Abstracts: The 5th Open German-Russian Workshop on Pattern Recognition and Image Understanding (FORWISS, Herrsching, Germany, 1998), pp. 49–51.

  130. I. B. Gurevich, Yu. I. Zhuravlev, and Yu. G. Smetanin, “On mathematical models related to automated safety systems for subway platforms,” in Collection of Abstracts: The 5th Open German-Russian Workshop on Pattern Recognition and Image Understanding (FORWISS, Herrsching, Germany, 1998), pp. 56–58.

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  139. I. B. Gurevitch, “On specificity of defining of pattern recognition procedures for biomedical images analysis,” in Proc. All-Union Conf. on Biomedical Informatics: Methodology (Scientific Council on Cybernetics, Acad. Sci. USSR, Moscow, 1986) pp. 34–36.

  140. I. B. Gurevitch, “The definition of the class of algorithms based on two-dimensional data estimates calculation for image recognition problems,” in Techniques and Means for Graphical Information Processing: Interuniversity Collection of Papers, Ed. by Yu. G. Vasin (Gorkovsk. Gos. Univ. im. N.I. Lobachevskogo, Gorky, 1986), pp. 47–66.

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  142. I. B. Gurevitch, “Vision as an information process,” in A Computational Investigation into the Human Representation and Processing of Visual Information Translation into Russian, Ed. by I. B. Gurevitch (Radio i Svyaz’, Moscow, 1987), pp. 5–17.

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  144. I. B. Gurevitch, “The optimization of an image recognition algorithm selection by the entropy filtration,” in Computer Analysis of Images and Patterns (CAIP’87): Abstracts, Wismar, DDR, 1987 (Kammer der Technik, Berlin, 1987), pp. 149–150.

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  146. I. B. Gurevitch, “The main principles of the descriptive theory of image recognition,” in Proc. All-Union Conf. on Artificial Intelligence (Pereslavl-Zalessky, Yaroslavl oblast, 1988), Vol. 2, pp. 76–81.

  147. I. B. Gurevitch, “The problem of image recognition,” in Recognition, Classification, Forecasting. Mathematical Methods and their Application, Ed. by Yu. I. Zhuravlev (Nauka, Moscow, 1988), pp. 280–329.

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  149. I. B. Gurevitch, “Models of images in recognition problems,” in Mathematical Techniques of Pattern Recognition (MMPO-4). Proceedings of 4-th All-Union Conf. in 6 Parts, Part 3, Section 2: Recognition, Analysis, and Understanding of Images: Methodology, Theory, Methods and Means (Sectional Presentations), Riga, 1989 (Publishing Division MIPKRRiS under Latvian Council of Ministers, Riga, 1989), pp. 33–36.

  150. I. B. Gurevitch, “The descriptive framework for an image recognition problem,” in Proc. 6th Scandinavian Conf. on Image Analysis (Pattern Recognition Society of Finland, Oulu, Finland, 1989), Vol. 1, pp. 220–227.

  151. I. B. Gurevitch, “The image recognition problem: Mathematical set-up, characterization and categorization,” in Computer Analysis of Images and Patterns: Proceedings of the International Fair Conference (Third Int. Conf. on Automatic Image Processing) (Scientific-Technological Society for Measurement and Automatic Control, Leipzig, 1989), pp. 33–35.

  152. I. B. Gurevitch, “The reduction of an image to a recognizable form,” in Proc. First Int. Conf. on Information Technologies for Image Analysis and Pattern Recognition, Lvov, 1990 (Inst. Fiz. Mekh. Akad. Nauk Ukrainskoi SSR, Lvov, 1990), Vol. 1, pp. 53–64.

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  154. I. B. Gurevitch, “The class of descriptive logical transformations in image recognition tasks,” in Proc. 5th Int. Workshop Image Processing and Computer Optics (Samara, 1994), pp. 17–18.

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  160. I. B. Gurevitch, V. V. Nikolenko, and V. I. Robotishin, “The design of mathematical tools for design automatization of image processing and recognition applied software packages,” in Image Processing Automatized Systems (ASOIz-86), Proc. 2nd All-Union Conf., Lvov, 1986 (Nauka, Moscow, 1986), pp. 330–331.

  161. I. B. Gurevitch and V. V. Nikolenko, “The multilevel approach to the problem template image search,” in Image Processing Automatized Systems (ASOIz-86), Proc. 2D All-Union Conf., Lvov, 1986 (Nauka, Moscow, 1986), pp. 123–124.

  162. I. B. Gourevitch and N. S. Polikarpova, “An efficient technique for image matching based on fractal dimension,” in Pattern Recognition and Image Analysis: New Information Technologies, Proc. 3rd Conf. (Nizhny Novgorod, 1997), Vol. 1, pp. 149–154.

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  165. I. B. Gourevitch, N. S. Polikarpova, and Yu. I. Zhuravlev, “Logical models of images and recognition operators for image understanding environment,” in Proc. 10th Scandinavian Conf. on Image Analysis (Pattern Recognition Society of Finland, Lappeenranta, Finland, 1997), Vol. 2, pp. 809–816.

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ACKNOWLEDGMENTS

The authors express their gratitude to I.V. Koryabkina for preparing review material on image features (Subsections 3.1.1, 3.1.2) and describing the method for selecting image transformations depending on the information characteristics of images in recognition problems (Subsection 3.2.3).

Funding

The study was carried out within the planned topic of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences (code 0063-2019-0003) at the stage descriptive models and representations in image analysis.

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    I. B. Gurevich & V. V. Yashina

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  1. I. B. Gurevich
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Igor’ B. Gurevich. Born August 24, 1938. Dr.-Eng. diploma engineer (Automatic Control and Electrical Engineering), 1961, National Research University Moscow Power Engineering Institute, Moscow, USSR; Dr. (Mathematical Cybernetics), 1975, Moscow Institute of Physics and Technology (National Research University), Moscow, USSR. Leading Researcher at Federal Research Center “Computer Science and Control” RAS, Moscow, Russian Federation. Worked from 1960 until present as engineer, researcher and lecturer in industry, research institutions, medicine, universities, and since 1985 in the USSR/Russian Academy of Sciences. Area of expertise: mathematical theory of image analysis, image mining, image understanding, mathematical theory of pattern recognition, theoretical computer science, medical informatics, applications of pattern recognition and image analysis techniques in biology, medicine and in automation of scientific research, and knowledge-based systems.

Gurevich suggested, proved, and developed with his pupils the Descriptive Approach to Image Analysis and Recognition (DAIA). Within DAIA, a new class of image algebra was introduced, defined and investigated (Descriptive Image Algebras); new types of image models were introduced, classified and investigated; axioms of Descriptive Theory of Image Analysis were introduced; a common model of image recognition process was defined and investigated; new settings of image analysis and recognition problems were introduced; the concept of “image equivalence” was introduced and investigated; new classes of image recognition algorithms were defined and investigated; an image formalization space was introduced, defined and investigated.

Listed results were used in development of software kits for image analysis and recognition and for solution of important and difficult applied problems of automated bio-medical image analysis.

Author of 2 monographs and 307 papers in peer-reviewed journals and proceedings indexed in Web of Science, Scopus, and Russian Science Citation Index on the platform Web of Science; 31 invited papers at international conferences, holder of 8 patents. Web of Science: 22 papers; SCOPUS: 76 papers, 287 citations in 148 documents; Hirsh index is 10; Russian Science Citation Index on the platform of Web of Science: 129 papers; 910 citations; Hirsh index 11.

Vice-Chairman of National Committee for Pattern Recognition and Image Analysis of Russian Academy of Sciences, Member of the International Association for Pattern Recognition (IAPR) Governing Board (representative from RF), IAPR Fellow. He has been the PI of 63 R&D projects as part of national and international research programs. Vice-Editor-in-Chief of Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, an international journal of the RAS; he is member of editorial boards of several international scientific journals, member of the program and technical committees of many international scientific conferences. Teaching experience: Lomonosov Moscow State University, RF (assistant professor), Dresden Technical University, Germany (visiting professor), George Mason University, USA (research fellow). Supervisor of six PhD students and many graduate and master students.

Vera V. Yashina. Born September 13, 1980. Diploma mathematician, Moscow Lomonosov State University (2002). Dr. (Theoretical Foundations of Informatics), 2009, Dorodnicyn Computing Center RAS, Moscow. Leading researcher in the Department of Recognition, Security and Analysis of Information at Federal Research Center Computer Science and Control RAS Sciences, Moscow, Russian Federation. Worked from 2001 to present in Russian Academy of Sciences. Scientific expertise: mathematical theory of image analysis, image algebras, models and medical informatics.

Main results obtained in mathematical theory of image analysis: descriptive image algebras with one ring were defined, classified, and investigated; new topological image formalization space was specified and investigated; descriptive generating trees were defined, classified and investigated. Listed results applied in biomedical image analysis.

Scientific secretary of National Committee for Pattern Recognition and Image Analysis of Presidium RAS. Member of Educational and Membership Committees of International Association for Pattern Recognition. Vice Chair of Technical Committee no. 16 Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis of International Association for Pattern Recognition. Member of many R&D projects as part of national and international research programs. Member of editorial board of Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, international journal of the RAS. Author of 79 papers in peer-reviewed journals, conference, and workshop proceedings. Web of Science: 11 papers; Hirsh index 4; SCOPUS: 40 papers, 162 citations in 75 papers; Hirsh index 8; Russian Science Citation index on platform Web of Science: 56 papers; 255 citations; Hirsh index 9.

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Gurevich, I.B., Yashina, V.V. Descriptive Image Analysis. Pattern Recognit. Image Anal. 33, 784–839 (2023). https://doi.org/10.1134/S1054661823040181

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