Publication de 12 numéros par an
ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009
Indexed in
USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION
RÉSUMÉ
The results of image classification problem solving using structural methods in computer vision systems are presented. The technology for introducing fuzzy clustering on a set of descriptors of keypoints for a dataset of image etalons has been developed. The usage of membership function values obtained by the results of fuzzy clustering is proposed, which makes it possible to calculate individual numerical characteristics for classification of the images being recognized. Mathematical and software classification models for data analysis based on the values of structural descriptions are developed, the properties and features of the usage of these models are investigated, and the effectiveness based on the results of processing specific images is evaluated. The classification efficiency using calculated weights for the descriptor system has been experimentally confirmed. Based on the research, it was concluded that classification technologies based on the fuzzy characteristics of etalon data are effective for compression of the space of structural features and reducing computational costs.
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Gorokhovatskyi, V.A., (2018) Image Classification Methods in the Space of Descriptions in the Form of a Set of the Key Point Descriptors, Telecommunications and Radio Engineering, 77(9), pp. 787-797.
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Gorokhovatskiy, V.A., (2011) Compression of Descriptions in the Structural Image Recognition, Telecommunications and Radio Engineering, 70(15), pp. 1363-1371.
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Tvoroshenko, I.S., and Gorokhovatsky, V.O., (2020) Effective tuning of membership function parameters in fuzzy systems based on multi-valued interval logic, Telecommunications and Radio Engineering, 79(2), pp. 149-163.
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Dang, T.H., Mai, D.S., and Ngo, L.T., (2019) Multiple kernel collaborative fuzzy clustering algorithm with weighted super-pixels for satellite image land-cover classification, Engineering Applications of Artificial Intelligence, 85, pp. 85-98.
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Saha, I., Sarkar, J.P., and Maulik, U., (2019) Integrated rough fuzzy clustering for categorical data analysis, Fuzzy Sets and Systems, 361, pp. 1-32.
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Gorokhovatskiy, V.A.,Vasilchenko, A.A., Manko, K.P., and Ponomarenko, R.P., (2018) Research of modifications of the method for establishing the relevance of images of objects according to descriptions in the form of a set of descriptors of keypoints, Systemy Upravlinnya, Navigatsii ta Zvyazku, 5(51), pp. 74-78, (in Ukrainian).
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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.
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Bian, T., (2020) An ensemble image quality assessment algorithm based on deep feature clustering, Signal Processing: Image Communication, 81, pp. 115703.
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Tvoroshenko, I.S. and Kramarenko, O.O., (2019) Software determination of the optimal route by geoinformation technologies, Radio Electronics Computer Science Control, 3, pp. 131-142.
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Tvoroshenko, I.S. and Gorokhovatsky, V.O., (2019) Intelligent classification of biophysical system states using fuzzy interval logic, Telecommunications and Radio Engineering, 78(14), pp. 1303-1315.
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D'urso, P. and Massari, R., (2019) Fuzzy clustering of mixed data, Information Sciences, 505, pp. 513-534.
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Choy, S.K., Yuen, K., and Yu, C., (2019) Fuzzy bit-plane-dependence image segmentation, Signal Processing, 154, pp. 30-44.
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Feng, L., Li, H., Gao, Y., and Zhang, Y., (2020) A color image segmentation method based on region salient color and fuzzy c-means algorithm, Circuits, Systems, and Signal Processing, 39(2), pp. 586-610.
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Hanyu, E., Cui, Y., Pedrycz, W., and Li, Z., (2019) Enhancements of rule-based models through refinements of Fuzzy C-Means, Knowledge-Based Systems, 170, pp. 43-60.
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Ma, L., Zhenhong, J., Yang, J., and Kasabov, N., (2020) Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering, European Journal of Remote Sensing, 53(1), pp. 1-13.
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Bagherinia, A., Minaei-Bidgoli, B., Hossinzadeh, M., and Parvin, H., (2019) Elite fuzzy clustering ensemble based on clustering diversity and quality measures, Applied Intelligence, 49(5), pp. 1724-1747.
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Mojarad, M., Nejatian, S., Parvin, H., and Mohammadpoor, M., (2019) A fuzzy clustering ensemble based on cluster clustering and iterative fusion of base clusters, Applied Intelligence, 49(7), pp. 2567-2581.
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Wu, T., Zhou, Y., Xiao, Y., Needell, D., and Nie, F., (2019) Modified fuzzy clustering with segregated cluster centroids, Neurocomputing, 361, pp. 10-18.
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Hashemzadeh, M., Oskouei, A.G., and Farajzadeh, N., (2019) New fuzzy C-means clustering method based on feature-weight and cluster-weight learning, Applied Soft Computing, 78, pp. 324-345.
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Gad-Elrab, A.A. and Noaman, A.Y., (2020) A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud-fog environment, Future Generation Computer Systems, 103, pp. 79-90.
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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
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Wang Chunyan, Wang Xiang, Wu Danfeng, Kuang Minchi, Li Zhengtong, Meticulous Land Cover Classification of High-Resolution Images Based on Interval Type-2 Fuzzy Neural Network with Gaussian Regression Model, Remote Sensing, 14, 15, 2022. Crossref