THE INTELLECTUAL ANALYSIS METHOD OF COLOR IMAGES
DOI:
https://doi.org/10.15588/1607-3274-2025-2-4Keywords:
intelligent image analysis, quantization, binarization, image feature extraction, clusteringAbstract
Context. Automatic and automated image analysis methods used in computer graphic design, biometric identification, and military target search are now widespread. The object of the research is the process of color image analysis.
Objective. The goal of the work is to create an intelligent method of image analysis based on quantization, binarization and clustering.
Method. The proposed method for intelligent color image analysis consists of the following techniques. The technique of reducing the number of colors based on the conversion of a color image into a gray-scale image and quantization of the resulting grayscale image improves the accuracy of image feature extraction by preventing the appearance of an excessive number of image clusters. The technique of creating a set of binary images based on binarization of a quantized gray-scale image allows increasing the speed of subsequent clustering by replacing sequential extraction of all elements of a quantized gray-scale image with parallel extraction of binary image elements, as well as separating clusters obtained during subsequent clustering by color due to image membership. The technique of determining the highest priority binary images based on the probability of occurrence of each color in the quantized gray-scale image improves the speed of image structure synthesis based on the analysis results by considering the most informative binary images. The technique of extracting binary image elements on the basis of its clustering allows to increase the accuracy of extracting binary image elements by improving the method of forming the neighborhoods of points (no radius of empirically determined neighborhood is needed), detecting random outliers and noise, extracting image elements of different shapes and sizes without specifying the number of extracted binary image elements, as well as increasing the speed of extracting binary image elements by forming the neighborhoods of white points only. The technique of determining the higher priority parts of the binary image based on the power of image clusters allows increasing the accuracy of image structure synthesis based on the analysis results by omitting noise and random outliers.
Results. The proposed method for intelligent analysis of color images was programmatically implemented using Parallel Computing Toolbox of Matlab package and investigated for the task of image feature extraction on the corresponding database. The results obtained allowed to compare the traditional and proposed methods.
Conclusions. The proposed method allows to expand the application area of color image analysis based on color-to-gray-scale image conversion, quantization, binarization, parallel clustering and contributes to the efficiency of computer systems for image classification and synthesis. Prospects for further research investigating the proposed method for a wide class of machine learning tasks
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