STRUCTURAL CLASSIFICATION IMAGES USING BAYESIAN DECISION MAKING
Keywords:computer vision, structural image recognition, set of structural attributes, SURF descriptors, relevance of descriptions, cluster representation of description, Bayesian formula, posteriori probability of classifying, proximity criterion of description in the base of etalons.
AbstractRelevance. Ensuring the effectiveness and multifunctionality of modern computer vision systems requires the creation of a variety of
effective methods for intellectual processing of visual information. The development of systems of structural recognition is directly connected
with the construction of new effective methods as well as the need to create a mechanism for assessing the effectiveness of these methods for
specific applications of visual data. Bayesian decision theory is one of the tools, based on the statistical characteristics of structural data. The
calculation of a posteriori probabilities of assigning a description of a visual object to a set of etalons makes it possible to directly perform the
process of recognition as well as preliminary evaluate the effectiveness of procedures for comparing or calculating the relevance of descriptions
with respect to a specific application image database. Special attention is paid to the study of the structure of the set of descriptors, which
directly affects the functioning of recognition systems.
Goal. Investigation of the possibility and peculiarities of the application of the statistical recognition theory in the decision-making
mechanism and the evaluation of effectiveness in the form of the probabilities of classifying an object description as class. Comparison of the
results of computations with experimental computer modeling data.
Method. A method of recognition based on the application of cluster characteristics of the image base using the Bayesian decision theory
is proposed. The result of investigation is the creation of a mechanism for evaluating the effectiveness of procedures for calculating the
relevance of descriptions with respect to the application image database.
Results. The main result of the paper is the confirmation of the fundamental relationship between methods of comparison with etalons
and the statistical approach in pattern recognition with respect to structural descriptions in the form of a set of characteristic features of
images represented by a cluster description. The statistical approach based on Bayesian estimates, which is simpler in sense of estimated costs,
can be used for preliminary calculations of recognition efficiency without costly experiments on software modeling.
The effectiveness of the developed method for calculating probabilistic estimates for applied image bases is proved. The result of the
classification demonstrated the universality and correctness of the application of the method, each of the test objects in several of the
examined image bases was correctly recognized.
The obtained numerical results of the computations are compared with the experimental data of computer modeling.Conclusions. In the conducted research the method of structural classification of images on the basis of a cluster representation of the
description by means of Bayesian decision theory is proposed. The basic idea of applying the corresponding mathematical approach is in
assigning the analyzed object to an etalon that has the greatest value of a posteriori probability. The developed method provides a sufficient
level of discrimination of images, which was confirmed by the described calculations and simulation results, is offered. Mechanism for
evaluating the effectiveness of the analyzed methods of structural recognition within the framework of the applied image database has been
The scientific novelty of the research consists in the synthesis of a new method of structural recognition of images and preliminary
estimation of efficiency by using the means of Bayesian decision theory and constructing classificatory solutions in the space of a clusteretalon.
The practical significance of the work is the obtaining of applied computational models for the application of the methods of structural
recognition and confirmation of their effectiveness in specific applied image bases.
Gorokhovatskiy V. A. Strukturnyy analiz i intellektual’naya
obrabotka dannykh v komp’yuternom zrenii. Moscow,
Kompaniya SMIT, 2014, 316 p.
Gorokhovatskiy V. A., Gorokhovatskiy A. V., Berestovskiy A. Ye. Strukturnoye raspoznavaniye izobrazheniy s primeneniyem
modeley intellektual’noy obrabotki i samoorganizatsii priznakov, Radio Electronics, Computer Science, Control, 2016, No. 3 (38), pp. 39–46.
Gorokhovatskiy V. A., Kulikov YU. A. Formalizm mul’timnozhestv v zadachakh strukturnogo raspoznavaniya i poiska v bazakh videodannykh, Iskusstvennyy intellekt, 2012, No. 1, pp. 76–85.
Duda R. O., Hart P. E., Stork D. G. Pattern classification. 2ed., Wiley, 2000, 738 p.
Shlezinger M. I. Matematicheskiye sredstva obrabotki izobrazheniy. Kiev, Naukova dumka, 1989, 200 p.
Bay H., Tuytelaars T., Van Gool L. Surf: Speeded up robust features, Computer Vision: Ninth European Conference on Computer Vision, Graz, 7–13 May, 2006: proceedings. Berlin, Springer, 2006, pp. 404–417.
Karami E., Prasad S., Shehata M.. Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images. Rezhim dostupa https://www.researchgate.net/publication/ 292157133_
Gorokhovatskiy V. A., Stolyarov V. S. Klassifikatsiya izobrazheniy na osnove klasternogo predstavleniya strukturnykh opisan, Bionika intellekta, 2016, No. 2 (87), pp. 83–87.
Vapnik V. N., Chervonenkis A. YA. Teoriya raspoznavaniya obrazov (statisticheskiye problemy obucheniya). Moscow, Nauka, 1974, 416 p.
Gonsales R., Vuds R.; [Per. s angl.]. Tsifrovaya obrabotka
izobrazheniy. Moscow, Tekhnosfera, 2005, 1070 p.
Feller V. [Per. s angl.]. Vvedeniye v teoriyu veroyatnostey i yeye prilozheniya. T. 1. Moscow, Mir, 1984, 528 p.
Shapiro L. and Stockman G. Computer vision, Prentice Hall, 2001, 625 p.
Szeliski R. Computer Vision: Algorithms and Applications.
London: Springer, 2010, 979 p.
How to Cite
Copyright (c) 2018 S. V. Gadetska, V. A. Gorokhovatsky
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.