METHOD FOR ANALYSING COLOR IMAGES BASED ON DIGITAL SIGNAL PROCESSING AND MACHINE LEARNING
DOI:
https://doi.org/10.15588/1607-3274-2026-2-14Keywords:
image analysis, training template, quantization, binarization, clustering, machine learning, digital signal processingAbstract
Context. In recent decades, rapid advances in digital signal processing and artificial intelligence have greatly expanded capabilities in visual information analysis. A color image, as a complex multidimensional signal, carries geometric, spectral, and textural data about objects. Efficient processing of such data requires integrating filtering, segmentation, and transformation methods with machine learning algorithms to extract hidden patterns and meaningful features.
Objective. The purpose of this work is to develop a method for analyzing color images based on quantization, binarization, clustering, and selection of priority clusters.
Method. The proposed approach combines digital signal processing and machine learning to improve the accuracy and speed of extracting informative elements. It includes several interrelated techniques: Quantization of three-color components in the training template to reduce color diversity and accelerate binary image formation; selection of priority template colors based on occurrence probability with a normalized threshold, enhancing feature detection accuracy; quantization of the original image’s color components to optimize segmentation and avoid excessive clustering; construction of a binary image using quantized template colors to eliminate false clusters and improve clustering precision; extraction of binary elements via clustering, verifying only white-point surroundings to suppress noise and automatically identify elements of various shapes; selection of priority binary elements using probabilistic assessment to enhance reliability.
Results. The method was implemented in Matlab and tested on a specialized database. Compared with traditional approaches, it demonstrated higher accuracy and stability in element extraction while reducing processing time through color optimization and removal of redundant clusters.
Conclusions. The comprehensive application of quantization, binarization, clustering, and priority element selection ensures accurate, fast, and adaptive analysis of color images. The method expands the functionality of visual information processing systems and can be used for statistical analysis, intelligent image processing, segmentation, and classification of complex visual structures
References
Song X. The Application of Computer Post-Processing Technique in the Abstract Photography Art Expression. 2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015), Shaanxi, China, 21–22 March 2015. Paris, France, 2015, pp. 1340–1343. DOI: 10.2991/etmhs-15.2015.296
Xu T. B., Kalantari S. Visual Flow-Based Programming Plugin for Brain-Computer Interface in Computer-Aided Design. Automation in Construction, 2024, Vol. 166, P. 105610. DOI: 10.1016/j.autcon.2024.105610
Khramova-Baranova O. L., Kudrevich V. V., Baranov H., Zvonkova H. Emergence and Evolution of Computer Design in Ukraine and the World. 2023 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). Kyiv, Ukraine, 13–18 November 2023, pp. 364–368. DOI: 10.1109/UkrMiCo61577.2023.10380346
Li S. Z. ed. By, Jain A. K. Handbook of Face Recognition. London, Springer, 2011, 699 p. DOI: 10.1007/978-0-85729-932-1
Karapetyan A., Fedorov E., Miroshkina I., Palahina O., Nesterenko A. Human Age Recognition Method Based on Facial Images Using an Ensemble of Neural Network Classifiers. Lecture Notes on Data Engineering and Communications Technologies. Cham, 2024, pp. 143–155. DOI: 10.1007/978-3-031-71804-5_10
Alves V., Santos J. M. dos, Pinto E., Ferreira I. M. P. L. V. O., Lima V. A., Felsner M. L. Digital Image Processing Combined with Machine Learning: A New Strategy for Brown Sugar Classification. Microchemical Journal, 2023, P. 109604. DOI: 10.1016/j.microc.2023.109604
Simão C., Nascimento J. E. de Melo, Arnaut de Toledo V. de Alencar , Lima V. A. de, Felsner M. L. Digital Image Processing Combined with Machine Learning: A Novel Approach for Bee Pollen Classification. Food Research International, 2025, P. 116399. DOI:
1016/j.foodres.2025.116399
Bow S. T. (eds) Pattern Recognition and Image Preprocessing 2nd ed. CRC Press, 2002, 720 p. DOI: 10.1201/9780203903896
Gonzalez R., Woods R. Digital Image Processing ; 4th ed. Pearson International, 2017, 1024 p. URL: https://elibrary.pearson.de/book/99.150005/9781292223070
Geman S., Geman D. Stochastic Relaxation, Gibbs Distributions and Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, Vol. 6, Issue 6, pp. 721–741.URL: http://image.diku.dk/imagecanon/material/GemanPAMI84.pdf
Ballard D. H., Brown C. M. Computer Vision, PrenticeHall, 1982, 523 p. URL: http://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/Ballard__D._and_Brown__C._M.__1982__Computer_Vision.pdf
Martin D. R., Fowlkes C. C., Malik J. Learning to Detect Natural Image Boundaries using Local Brightness, Color, and Texture Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, Vol. 26, no. 5, pp. 530–549. DOI: 10.1109/TPAMI.2004.1273918
Dorin C., Meer P. Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, Vol. 24, Issue 5, pp. 603–619. URL: https://courses. csail.mit.edu/6.869/handouts/PAMIMeanshift.pdf
Kass M., Witkin A., Terzopoulos D. Snakes: Active Contour Models. International Journal of Computer Vision, 1988, Vol. 1, Issue 4, pp. 321–331.URL: http://www.cs.ait.ac.th/~mdailey/cvreadings/KassSnakes.pdf
Shapiro L. G., Stockman G. C. Computer Vision. New Jersey, Prentice-Hall, 2001, pp. 279–325. URL:https://cdn.preterhuman.net/texts/science_and_technology /artificial_intelligence/ Computer%20Vision%20-%20Linda%20Shapiro.pdf
Wu Z., Leahy R. An Optimal Graph Theoretic Approach to Data Clustering: Theory and its Application to Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, Vol. 15, Issue 11, pp. 1101–1113. URL:
https://cse.sc.edu/~songwang/csce790/Suppl/WuLeahy.pdf
Chan T. F., Vese L. Active Contours without Edges. IEEE Transactions on Image Processing, 2001, Vol. 10, Issue 2, pp. 266–277. URL:
https://www.math.ucla.edu/~lvese/PAPERS/IEEEIP2001.pdf
Fu Z., Wang L. Color Image Segmentation Using Gaussian Mixture Model and EM Algorithm. Multimedia and Signal Processing. Berlin, Heidelberg, 2012, pp. 61–66. DOI: 10.1007/978-3-642-35286-7_9
Brusco M. J., Shireman E., Steinley D. A Comparison of Latent Class, K-means, and K-median Methods for Clustering Dichotomous Data. Psychological Methods, 2017, Vol. 22, Issue 3, pp. 563–580. DOI: 10.1037/met0000095
Ball G. H., Hall D. J. ISODATA, a Novel Method of Data Analysis and Pattern Classification. Menlo Park, CA, Stanford Research Institute, 1965, 79 p. URL: https://apps.dtic.mil/sti/tr/pdf/AD0699616.pdf
Bezdek J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Boston, MA, Springer US, 1981, 256 p. DOI: 10.1007/978-1-4757-0450-1
Aggarwal C. C., Reddy C. K..Data Clustering: Algorithms and Applications. Boca Raton, FL, CRC Press, 2014, 620 p.
URL: https://people.cs.vt.edu/~reddy/papers/DCBOOK.pdf
Mirkin B. G. Clustering for Data Mining: A Data Recovery Approach. Boca Raton, FL, CRC Press, 2005, 277 p. URL:
http://www.dcs.bbk.ac.uk/~mirkin/papers/n.pdf
Ester M., Kriegel H.-P., Sander J., Xu X. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD’1996), 1996, pp. 226–231. URL:
https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf
Ankerst M., Breunig M. M., Kriegel H.-P., Sander J. OPTICS: Ordering Points to Identify the Clustering Structure. Proceedings of ACM SIGMOD’99 Int. Conf. on Management of Data. Philadelphia PA, 1999, pp. 49–60.URL: https://www.dbs.ifi.lmu.de/Publikationen/Papers/OPTICS.pdf
Fedorov E., Utkina T., Neskorodieva T. Intellectual Identification Method of the Egg Development State Based on Deep Neural Nets. Proceedings of International Conference on Data Science and Applications. Singapore, 2023, pp. 159–174. DOI: 10.1007/978-981-19-6634-7_12
Shamshiri S., Liu H., Sohn I. Adversarial Robust Image Processing in Medical Digital Twin. Information Fusion, 2024, P. 102728. DOI: 10.1016/j.inffus.2024.102728
Bannore V. Iterative-Interpolation Super-Resolution Image Reconstruction. A Computationally Efficient Technique. Berlin, Heidelberg, Springer Berlin Heidelberg, 2009, 113 p. DOI: 10.1007/978-3-642-00385-1
Hidayat E. Y., Hastuti Kh., Muda A. K. Artificial Intelligence in Digital Image Processing: A Bibliometric Analysis. Intelligent Systems with Applications, 2024, P. 200466. DOI: 10.1016/j.iswa.2024.200466
Chauhan S., Saini P., Sofat S. Brain Tissue Segmentation from MRI Scans using Digital Image Processing. Procedia Computer Science, 2025, Vol. 260, pp. 32–39. DOI: 10.1016/j.procs.2025.03.174
Fedorov E. E., Khramova-Baranova O. L., Utkina T. Y., Kozhushko R. Y., Nesen I. O. The Intellectual Analysis Method of Color Images. Radio Electronics, Computer Science, Control, 2025, no. 2, pp. 45–55. DOI: 10.15588/1607-3274-2025-2-4
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 E. E. Fedorov, O. L. Khramova-Baranova, T. Y. Utkina, H. V. Galytska, I. O. Nesen
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
