Application of multi-component data model for class descriptions in the image classification problem
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Abstract
The subject of research of the article is the methods of image classification according to the set of descriptors of key points in computer vision systems. The aim is to increase the efficiency of classification by introducing a multicomponent data model on a set of descriptors for the base of reference images. Applied methods: ORB detector and descriptors, apparatus of set theory and vector space, metric models for determining the relevance of sets of multidimensional vectors, elements of probability theory, software modeling. Results are obtained: a modified method of image classification based on the introduction of a multicomponent model for data analysis with a system of centers is developed, methods of constructing a set of data centers are identified, the most effective is the set medoid and centers based on it. The effectiveness of the modification significantly depends on the method of forming the centers, the applied classification model, as well as on the data itself. The best results were shown by the classification with the integrated indicator separately for each of the standards in the form of the sum of the values of the distributions for the set of centers; experimentally tested the effectiveness of the classification, confirmed the efficiency of the proposed method. The practical significance of the work is the construction of classification models in the transformed data space, confirmation of the efficiency of the proposed modifications on the examples of images, the creation of software for the implementation of developed classification methods in computer vision systems.
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Daradkeh, Y.I., Gorokhovatskyi, V., Tvoroshenko, I., Gadetska, S., and Al-Dhaifallah, M. Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Compo-nents, IEEE Access, 2021, 9, pp. 92964-92973, DOI: https://doi.org/10.1109/ACCESS.2021.3093457.
Gorokhovatsky V.О, Gadetska S.V., Stiahlyk N.I., Vlasenko N.V. Classification of images based on an ensemble of statistical distributions by classes of etalons for structural description components. Radio Electronics, Computer Science, Control, 2020, №4, p. 85–94. DOI https://doi.org/10.15588/1607-3274-2020-4-9.
Gorokhovatskyi, V., Ponomarenko, R. Classification of images based on the formation of independent cluster system within the structural descriptions of etalon dataset. Advanced Information Systems, 2020, Vol. 4, No. 2, pp. 17-23, doi: https://doi.org/10.20998/2522-9052.2020.2.04.
Gorokhovatskyi V.O., Gadetska S.V. Statistical processing and data mining in structural image classification methods (monograph), 2020, Kharkiv, FLP Panov A.N., 128 p., DOI: https://doi.org/10.30837 / 978-617-7859-69-6.
Gorokhovatskyi, V., Gadetska, S., Zhadan, O., Khvostenko, O. Study of the effectiveness of image classifiers by statis-tical distributions for components of structural description. Advanced Information Systems, 2021, Vol. 5, No. 1, pp. 5-11, doi: https://doi.org/10.20998/2522-9052.2021.1.01.
Flach, P. Machine learning. The Art and Science of Algorithms that Make Sense of Data. New York, NY, USA: Cam-bridge University Press, 2012.
Kohonen, T. Self-Organizing Maps. Springer-Verlag, Berlin, Heidelberg (2001). doi: book/10.5555/558021.
Nasirahmadi, A., Ashtiani, S.H.M.: Bag-of-Feature model for sweet and bitter almond classification. Biosystems engi-neering. 156, 51–60 (2017). doi: https://doi.org/10.1016/j.biosystemseng.2017.01.008.
Hietanen, A., et al.: A comparison of feature detectors and descriptors for object class matching. Neurocomputing. 184, 3–12 (2016). doi: https://doi.org/10.1016/j.neucom.2015.08.106.
Kim S. Biologically motivated perceptual feature: Generalized robust invariant feature / S. Kim, I.-S. Kweon. – In Asian Conf. of Comp. Vision (ACCV-06). – 2006. – P. 305–314.
Svyrydov, A., Kuchuk, H., Tsiapa, O. Improving efficienty of image recognition process: Approach and case study, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DES-SERT 2018, pp. 593-597, doi: http://dx.doi.org/10.1109/DESSERT.2018.8409201.
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.
Nong Ye. Data Mining: Theories, Algorithms, and Examples (1st. ed.). CRC Press, Inc., USA – 2013.
Gorokhovatskyi, O., Gorokhovatskyi, V., and Peredrii, O., Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features, Data, 2018, 3(4), 52. – doi: https://doi.org/10.3390/data3040052.
Yakovleva, O., Nikolaieva, K. “Research of descriptor based image normalization and comparative analysis of SURF, SIFT, BRISK, ORB, KAZE, AKAZE descriptors“, Advanced Information Systems, 2020, Vol. 4, No. 4, pp. 89-101.
Oliinyk, A., Subbotin, S., Lovkin, V., Blagodariov, O., Zaiko, T. The System of Criteria for Feature Informativeness Estimation in Pattern Recognition. Radio Electronics, Computer Science, Control, 2017, №4, pp. 85-96.
Leskovets, Yu., Radzharaman, A., Ulman, Dzheffry D. (2016) Analyzing large datasets, Moscow, DMK Press, 2016. 498 p.
Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang. Learning Discriminative and Transformation Covariant Local Feature Detectors. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6818-6826.
Duda R.O., Hart P.E., Stork D.G. Pattern classification, 2ed., Wiley, 2000.–738 p.
Kinoshenko D., Mashtalir V., Yegorova E., Vinarsky V. Hierarchical partitions for content image retrieval from large-scale database. Machine Learning and Data Mining in Pattern Recognition / Perner P., Imlya A. (Eds.). – Lecture Notes in Artificial Intelligence. – Springer-Verlag. – Vol. 3587 – 2005. – P. 445−455.
Gorokhovatskyi, V. & Vlasenko, N. (2021), “The image description reduction in the set of descriptors on informa-tiveness metric criteria base”, Advanced Information Systems, Vol. 5, No. 4, pp. 10-16, doi: https://doi.org/10.20998/2522-9052.2021.4.02.
Q. Bai, S. Li, J. Yang, Q. Song, Z. Li & X. Zhang (2020), “Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey,” IEEE Access, vol. 8, pp. 855–879, doi: https://doi.org/10.1109/ACCESS.2020.3028740.
Celik, C. and Sakir, H. Content based image retrieval with sparse representations and local feature descriptors: A com-parative study, Pattern Recognit., vol. 68, pp. 1–13, Aug. 2017, doi: https://doi.org/10.1016/j.patcog.2017.03.006.
Peters, J.F. Foundations of Computer Vision. ISRL, vol. 124. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52483-2.
OpenCV Open Source Computer Vision, Accessed: Jun. 28, 2021. Available: https://docs.opencv.org/master/index.html.
Emgu CV: OpenCV in .NET (C#, VB, C++ and more), Accessed: Jan. 12, 2022. Available: https://www.emgu.com/wiki/index.php/Main_Page.