THE STUDY OF STATISTICAL PROPERTIES OF THE MODEL BLOCK REPRESENTATION FOR SET OF DESCRIPTORS OF KEY POINTS OF IMAGES

Authors

  • V. A. Gorokhovatsky National University of Radio Electronics, Kharkіv, Ukraine
  • S. V. Gadetska Kharkiv Educational and Scientific Institute of SHEI “Banking University”, Kharkіv, Ukraine
  • N. I. Stiahlyk Kharkiv Educational and Scientific Institute of SHEI “Banking University”, Kharkіv, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2019-2-11

Keywords:

computer vision, structural image recognition, multiple key points, BRISK descriptors, descriptive relevance, block representation, statistical distribution, chi-square test, Renyi divergence, sign criterion, significance of the difference of descriptions.

Abstract

Context. The multidimensional nature of the processed data in modern computer vision systems requires new approaches to the
construction of effective feature spaces that simplify processing by summarizing the available information. Structural methods of
image recognition use descriptions of visual objects in the form of sets of key point descriptors as sets of numerical vectors of high
dimension. The main tool for reducing the dimension is the presentation of data in the form of a system of their blocks and a statistical
study of such data structures, which in terms of recognition should display the generalized properties of an object as a set of its
fragments. In this connection, there is problem of studying the features of the applied application and the characteristics of the block
representation model in the aspect of its use to determine the relevance of descriptions and classify data within the base of reference
images.
Objective. Perform statistical estimation of the importance of making classification decisions on the basis of the calculation of
the relevance of object descriptions for the model of block representation of descriptors of key points of images.
Method. Methods are proposed for distinguishing descriptions based on the application of the block representation model of the
data descriptors of key points of images using the criteria of mathematical statistics and information theory tools.
Results. The main result of the article is the confirmation that the use of classical statistical criteria for analyzing empirical data
in the form of structural descriptions of images makes it possible to determine the quality factor of the constructed feature space for
distinguishing visual objects when they are recognized in computer vision systems. The introduction of the block representation
model and statistical analysis for the values of descriptors of key features of images contributes to the efficiency of the process of
recognizing visual objects, which is confirmed by the improvement in the level of difference by increasing the fragment size in the
constructed description chain structure.
Conclusions. The use of a variety of statistical criteria gave an identical effect on the significance of the differences in the empirical
descriptions of visual objects in the constructed feature space, which underlines the objectivity of the study. The implemented
model of block representation of data retains the distinguishing properties of a structural description with the effect of a significant
improvement in the speed of classification decision making.
The scientific novelty of the study is the improvement and statistical substantiation of models for making decisions about the
class of visual objects based on the calculation of the relevance of their descriptions with references using a block representation of
descriptors of key points of images.
The practical significance of the work is confirming the advisability of introducing a block structure for the block description of
an object as an effective approach to solving the problem of recognition with examples of images for implementation in computer
vision systems.

Author Biographies

V. A. Gorokhovatsky, National University of Radio Electronics, Kharkіv

Dr. Sc., Professor, Professor of the Department of Computer Science,

S. V. Gadetska, Kharkiv Educational and Scientific Institute of SHEI “Banking University”, Kharkіv

PhD, Associate Professor, Head of the Department of Information Technologies

N. I. Stiahlyk, Kharkiv Educational and Scientific Institute of SHEI “Banking University”, Kharkіv

PhD, Associate Professor of the Department of Information Technologies

References

Gorohovatskij V. A. Strukturnyj analiz i intellektual’naja obrabotka dannyh v komp’juternom zrenii. Harkiv,

Kompanija SMIT, 2014, 316 p.

Gorohovatskij V. A. Gorohovatskij A. V., Berestovskij A.E. Strukturnoe raspoznavanie izobrazhenij s primeneniem modelej intellektual’noj obrabotki i samoorganizacii priznakov, Radio Electronics, Computer Science, Control, 2016, No. 3 (38), pp. 39–46.

Gorokhovatsky V.A. Efficient Estimation of Visual Object Relevance during Recognition through their Vector

Descriptions, Telecommunications and Radio Engineering. 2016, Vol. 75, No 14. pp. 1271–1283.

Gorokhovatsky V. O. and Gadetska, S. V., Determination of Relevance of Visual Object Images by Application of

Statistical Analysis of Regarding Fragment Representation of their Descriptions, Telecommunications and Radio

Engineering, 2019, 78 (3), pp. 211–220.

Stefan Leutenegger, Margarita Chli, Roland Y. Siegwart. BRISK: Binary Robust Invariant Scalable Key-points, 2011,

Computer Vision (ICCV), pp. 2548–2555,

Gorohovats’kyj V. O., Gadec’ka S. V., Ponomarenko R. P. Statystychni rozpodily ta lancjuzhkove podannja danyh pry

vyznachenni relevantnosti strukturnyh opysiv vizual’nyh ob’jektiv, Systemy upravlinnja, navigacii’ ta zv’jazku, 2018.

No. 6 (52), pp. 87–92. DOI: 10.26906/SUNZ.2018.6.087

Gadetska S. V., Gorokhovatsky V. O. Statistical Measures for Computation of the Image Relevance of Visual Objects

in the Structural Image Classification Methods, Telecommunications and Radio Engineering, 2018, Vol. 77 (12), pp. 1041–1053.

Gorokhovatskyi V. Classification of Images of Visual Objects Based on Statistical Relevance Measures of Their

Structural Descriptions, Proc. the IEEE 13th International Scientific and Technical Conference on Computer Sciences

and Information Technologies (CSIT2018), 11–14 September 2018. Lviv, Ukraine, pp. 68–71.

Porter F. Testing Consistency of Two Histograms. [Electronic resource]. Access mode:

https://www.researchgate.net/publication/1917663_Testing_Consistency_of_Two_Histograms.

Renyi A. On measures of entropy and information. [Electronic resource]. Access mode:

http://l.academicdirect.org/Horticulture/GAs/Refs/Renyi_1961.pdf.

Alfred O. Hero, Bing Ma, Olivier Michel, John Gorman. Alpha-Divergence for Classification, Indexing and

Retrieval. [Electronic resource]. Access mode:https://pdfs.semanticscholar.org/c5fd/0dbd41a6b1ed8d78d5

ad02fcabd44666cc.pdf?_ga=2.24150137.2123089777.1551138070-1581667237.1550924667.

Fukunaga K. Vvedenie v statisticheskuju teoriju raspoznavanija obrazov. Moscow, Nauka, 1979, 367 p.

Vadzinskij R. Statisticheskie vychislenija v srede EXCEL. Biblioteka pol’zovatelja. SPb, Piter, 2008, 608 p.

Gmurman V. E. Teorija verojatnostej i matematicheskaja statistika. Moscow, Vyssh. shk, 2004, 479 p.

OpenCV Open Source Computer Vision. [Electronic resource]. Access mode:https://docs.opencv.org/master/index.html

Published

2019-05-28

How to Cite

Gorokhovatsky, V. A., Gadetska, S. V., & Stiahlyk, N. I. (2019). THE STUDY OF STATISTICAL PROPERTIES OF THE MODEL BLOCK REPRESENTATION FOR SET OF DESCRIPTORS OF KEY POINTS OF IMAGES. Radio Electronics, Computer Science, Control, (2), 100–107. https://doi.org/10.15588/1607-3274-2019-2-11

Issue

Section

Progressive information technologies

Most read articles by the same author(s)