Publication de 12 numéros par an
ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009
Indexed in
THE APPLICATION OF NON-PARAMETRIC STATISTICS METHODS IN IMAGE CLASSIFIERS BASED ON STRUCTURAL DESCRIPTION COMPONENTS
RÉSUMÉ
The results of the solution of image classification problems in computer vision systems using structural methods are presented. The models of statistical data estimation during the image relevance definition and classification were developed. A component-by-component presentation of descriptions with the estimation of mathematical expectation and variance values was proposed. The integrated criteria of data relevance based on these estimated values follow and implemented. The mathematical and software data processing and analysis models for the classification based on the structural description were developed, the properties of the application of these models were investigated, and the analysis of image processing efficiency was performed. The efficiency of classification using statistical estimations for the system of components for descriptors has been experimentally confirmed. Based on the research, a conclusion about the effectiveness of classification technologies using non-parametric evaluation for etalon data for the compression of the feature space and computational cost reduction was reached.
-
Gorokhovatskyi, V., Gadetska, S., and Stiahlyk, N., (2020) Image structural classification technologies based on statistical analysis of descriptions in the form of bit descriptor set, In CEUR Workshop Proceedings: Computer Modeling and Intelligent Systems (CMIS-2020), 2608, pp. 1027-1039.
-
Gorokhovatskyi, V., Gadetska, S., and Ponomarenko, R., (2019) Recognition of Visual Objects Based on Statistical Distributions for Blocks of Structural Description of Image, Lecture Notes in Computational Intelligence and Decision Making. Proceedings of the XV International Scientific Conference "Intellectual Systems of Decision Making and Problems of Computational Intelligence" (ISDMCI'2019), pp. 501-512.
-
Peters, J.F., (2017) Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection, Cham, Switzerland: Springer International Publisher, 417 p.
-
Gorokhovatskyi, V. and Tvoroshenko, I., (2020) Image Classification Based on the Kohonen Network and the Data Space Modification, In CEUR Workshop Proceedings: Computer Modeling and Intelligent Systems (CMIS-2020), 2608, pp. 1013-1026.
-
Gorokhovatskyi, V.A., (2016) Efficient Estimation of Visual Object Relevance during Recognition through their Vector Descriptions, Telecommunications and Radio Engineering, 75(14), pp. 1271-1283.
-
Gorokhovatskyi, V.O., Tvoroshenko, I.S., and Peredrii, O.O., (2020) Image classification method modification based on model of logic processing of bit description weights vector, Telecommunications and Radio Engineering, 79(1), pp. 59-69.
-
Gorokhovatskyi, V.O., Gadetska, S.V., and Stiahlyk, N.I., (2019) Study of statistical properties of the block representation model for a set of key image descriptors, Radio Electronics Computer Science Control, 2, pp. 100-107.
-
Duda, R.O., Hart, P.E., and Stork, D.G., (2000) Pattern Classification, Hoboken, USA: John Wiley & Sons, 738 p.
-
Leutenegger, S., Chli, M., and Siegwart, R., (2011) BRISK: Binary Robust Invariant Scalable Keypoints, Proceedings of 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548-2555.
-
Rublee, E., Rabaud, V., Konolige, K., and Bradski, G., (2011) ORB: an efficient alternative to SIFT or SURF, Proceedings of 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564-2571.
-
Sonka, M., Hlavac, V., and Boyle, R., (2014) Image Processing, Analysis, and Machine Vision, Atlanta, USA: Thomson-Engineering, 920 p.
-
Feller, V., (1984) Introduction to Probability Theory and its Applications, Moscow, Russia: World, 528 p., (in Russian).
-
Nong, Ye, (2013) Data Mining: Theories, Algorithms, and Examples, Florida, USA: CRC Press, 349 p.
-
Szeliski, R., (2010) Computer Vision: Algorithms and Applications, London, Great Britain: Springer-Verlag, 957 p.
-
Flah, P., (2015) Machine Learning. The Science and Art of Building Algorithms that Extract Knowledge from Data, Moscow, Russia: DMK Press, 400 p., (in Russian).
-
Porter, F., Testing Consistency of Two Histograms, https://www.researchgate.net/publication/ 1917663_Testing_Consistency_of_Two_Histograms.
-
Kacprzyk, J. and Pedrycz, W., (2015) Springer Handbook of Computational Intelligence, Berlin Heidelberg, Germany: Springer-Verlag, 1633 p.
-
Sharma, G. and Schiele, B., (2015) Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval, Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), pp. 7-13.
-
Daradkeh Yousef Ibrahim, Tvoroshenko Iryna, Gorokhovatskyi Volodymyr, Latiff Liza Abdul, Ahmad Norulhusna, Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic, IEEE Access, 9, 2021. Crossref