Abstract
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.
This work was supported in part by the Russian Foundation for Basic Research (Project No. 20-07-01031).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gurevich, I.B., Yashina, V.V.: Descriptive image analysis. genesis and current trends. Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 27(4), 653–674 (2017)
Gurevitch, I.B.: The descriptive framework for an image recognition problem. In: 6th Scandinavian Conference on Image Analysis, vol. 1, pp. 220–227 (1989). Pattern Recognition Society of Finland
Gurevich, I.: The descriptive approach to image analysis. current state and prospects. In: Kalviainen, J., Parkkinen, A.K. (eds.) 14th Scandinavian Conference on Image Analysis, LNCS, vol. 3540, pp. 214–223. Springer, Heidelberg (2005). https://doi.org/10.1007/11499145_24
Gurevich, I.B., Yashina, V.V.: Descriptive approach to image analysis: image models. Pattern Recog. Image Anal. Adv. Math. Theory Appl. 18(4), 518–541 (2008)
Gurevich, I.B., Yashina, V.V.: Descriptive approach to image analysis: image formalization space. Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications 22(4), 495–518 (2012)
Gurevich, I.B., Yashina, V.V.: Descriptive image analysis. foundations and descriptive image algebras. Int. J. Pattern Recogn. Artif. Intell. 33(12), 1940018-1–1940018-25 (2019)
Gurevich, I.B., Yashina, V.V.: Descriptive image analysis: III. multilevel model for algorithms and initial data combining in pattern recognition. Pattern Recogn. Image Anal. 30(3), 328–341 (2020)
Gurevich, I.B., Yashina, V.V.: Descriptive image analysis: part II. descriptive image models. Pattern Recogn. Image Anal. 29(4), 598–612 (2019). https://doi.org/10.1134/S1054661819040035
Gurevitch, I.B.: Image analysis on the base of reversing algebraic closure technique. In: The Problems of Artificial Intelligence and Pattern Recognition, Scientific Conference with Participation of Scientists from Socialistic Countries (Kiev, May 13–18 1984), pp. 41––43. V.M. Glushkov Institute of Cybernetics of the Academy of Sciences of the Ukrainian SSR (1984). [in Russian]
Gurevich, I.B., Yashina, V.V.: Computer-aided image analysis based on the concepts of invariance and equivalence. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 16(4), 564–589 (2006)
Gurevich, I.B., Yashina, V.V.: Descriptive image analysis: iii. multilevel model for algorithms and initial data combining in pattern recognition. Pattern Recogn. Image Anal.: Adv. Math. Theory Appl. 30(3), 328–341 (2020)
Gurevich, I.B., Yashina, V.V.: Dscriptive image analysis: part iv. information structure for generating descriptive algorithmic schemes for image recognition. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 30(4), 649–665 (2020)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20(3), 226–239 (1998)
Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 52–66. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_5
Zhuravlev, Y.I.: An algebraic approach to recognition and classification problems. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 8, 59–100 (1998)
Gurevich, I.B. Yashina V.V.: Descriptive image analysis. genesis and current trends. In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, Pleiades Publishing, Ltd. 27(4), 653–674 (2017)
Gurevich, I.B., Nefyodov, A.V.: Block diagram representation of a 2d-aec algorithm with rectangular support sets. Pattern Recogn. Image Anal. Adv. Math. Theory Appl. 15(1), 187–191 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gurevich, I., Yashina, V. (2021). Basic Models of Descriptive Image Analysis. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-68821-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68820-2
Online ISBN: 978-3-030-68821-9
eBook Packages: Computer ScienceComputer Science (R0)