The image description reduction in the set of descriptors on informativeness metric criteria base
Article Sidebar
Main Article Content
Abstract
The subject of the research is the methods of image classification on a set of key point descriptors in computer vision systems. The goal is to improve the performance of structural classification methods by introducing indexed hash structures on the set of the dataset reference images descriptors and a consistent chain combination of several stages of data analysis in the classification process. Applied methods: BRISK detector and descriptors, data hashing tools, search methods in large data arrays, metric models for the vector relevance estimation, software modeling. The obtained results: developed an effective method of image classification based on the introduction of high-speed search using indexed hash structures, that speeds up the calculation dozens of times; the gain in computing time increases with an increase of the number of reference images and descriptors in descriptions; the peculiarity of the classifier is that not an exact search is performed, but taking into account the permissible deviation of data from the reference; experimentally verified the effectiveness of the classification, which indicates the efficiency and effectiveness of the proposed method. The practical significance of the work is the construction of classification models in the transformed space of the hash data representation, the efficiency confirmation of the proposed classifiers modifications on image examples, development of applied software models implementing the proposed classification methods in computer vision systems.
Article Details
References
Gorokhovatskyi V.O., Gadetska S.V. (2020) Statistical processing and data mining in structural image classification methods (monograph), Kharkiv, FLP Panov A.N., 128 p., DOI: 10.30837 / 978-617-7859-69-6.
Gorokhovatskiy V.A. Compression of Descriptions in the Structural Image Recognition. Telecommunications and Radio Engineering. – 2011, Vol. 70, No 15. – P. 1363–1371.
Gadetska S., Gorokhovatskyi V., Stiahlyk N. (2020) STUDY OF STATISTICAL PROPERTIES OF THE BLOCK SUP-PLY MODEL FOR A NUMBER OF DECORATORS OF KEY POINTS OF IMAGES. Radio Electronics, Computer Science, Control, №3 , p. 78–87. – doi: 10.15588/1607-3274-2020-3-7.
Oliinyk, A., Subbotin, S., Lovkin, V., Blagodariov, O., Zaiko, T. The System of Criteria for Feature Informativeness Estimation in Pattern Recognition. Радіоелектроніка, інформатика, управління. – 2017. – № 4. – С. 85–96.
Kira, K. A practical approach to feature selection / K. Kira, L. Rendell // Machine Learning : International Conference on Machine Learning ML92, Aberdeen, 1-3 July 1992 : proceedings of the conference. – New York: Morgan Kauf-mann, 1992. – P. 249–256.
Computational intelligence: a methodological introduction / [R. Kruse, C. Borgelt, F.Klawonn et. al.]. – London : Springer-Verlag, 2013. – 488 p.
Nong Ye. Data Mining: Theories, Algorithms, and Examples (1st. ed.). CRC Press, Inc., USA – 2013
Daradkeh, Y.I., Gorokhovatskyi, V., Tvoroshenko, I., Gadetska, S., and Al-Dhaifallah, M. (2021) Methods of Classifi-cation of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components, IEEE Access, 9, pp. 92964-92973, DOI: 10.1109/ACCESS.2021.3093457
Gorokhovatskyi, O., Gorokhovatskyi, V., and Peredrii, O., (2018) Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features, Data, 3(4), 52. – doi: 10.3390/data3040052. Available online: https://www.mdpi.com/2306-5729/3/4/52
Kohonen, T., (2001) Self-Organizing Maps, Heidelberg, Berlin: Springer-Verlag, 502 p.
Leskovets, Yure, Radzharaman, Anand, Ulman, Dzheffry D. (2016) Analyzing large datasets, Moscow, DMK Press, 2016. – 498 p.
Q. Bai, S. Li, J. Yang, Q. Song, Z. Li, and X. Zhang, “Object Detection Recognition and Robot Grasping Based on Ma-chine Learning: A Survey,” IEEE Access, vol. 8, pp. 181855–181879, Oct. 2020, doi: 10.1109/ACCESS.2020.3028740.
P. Flach. Machine learning. The Art and Science of Algorithms that Make Sense of Data. New York, NY, USA: Cam-bridge University Press, 2012.
Stańczyk U. Feature Evaluation by Filter, Wrapper, and Embedded Approaches. In: Stańczyk U., Jain L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence. 2015. Springer, Berlin, Heidelberg, vol. 584, 568 p.
Gavrilenko, S.Yu., Sheverdin, I.V., Geiko, G.V. Assessment of informativeness and selection of features in identifying the state of the computer system. Modern information systems, 2021, т.5, No2, с.5-12, DOI: https://doi.org/10.20998/2522-9052.2021.2.01
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.
Robnik-Sikonja,M., Kononenko, I. (2003). Theoretical and empirical analisis of ReliefF and RReliefF. Machine Learn-ing 53 (1-2): 23-69.
Gorokhovatsky V.О., Gadetska S. V., Stiahlyk N. I., Vlasenko N. V. (2020) CLASSIFICATION OF IMAGES BASED ON AN ENSEMBLE OF STATISTICAL DISTRIBUTIONS BY CLASSES OF ETALONS FOR STRUCTUR-AL DESCRIPTION COMPONENTS. Radio Electronics, Computer Science, Control, №4 , p. 85–94. – DOI 10.15588/1607-3274-2020-4-9
A. V. Gorokhovatsky, V.A. Gorokhovatsky, A.N. Vlasenko, N.V. Vlasenko Quality Criteria for Multidimensional Object Recognition Based Upon Distance Matrices. Telecommunications and Radio Engineering. – 2014, Vol. 73, No 18. – P. 1661 – 1670.
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
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.
Q. Bai, S. Li, J. Yang, Q. Song, Z. Li, and X. Zhang, “Object Detection Recognition and Robot Grasping Based on Ma-chine Learning: A Survey,” IEEE Access, vol. 8, pp. 181855–181879, Oct. 2020, doi: 10.1109/ACCESS.2020.3028740.
C. Celik, and H. Sakir, “Content based image retrieval with sparse representations and local feature descriptors: A comparative study,” Pattern Recognit., vol. 68, pp. 1–13, Aug. 2017, doi: 10.1016/j.patcog.2017.03.006.
M. Ghahremani, Y. Liu, and B. Tiddeman, “FFD: Fast Feature Detector,” IEEE Trans. Image Process., vol. 30, pp. 1153–1168, Jan. 2021, doi: 10.1109/TIP.2020.3042057.
Liu Z. Large-scale CelebFaces Attributes (CelebA) Dataset. URL: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html.