Information Technologies and Systems (Інформаційні технології та системи), 2025, Том 1, № 1

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Browsing Information Technologies and Systems (Інформаційні технології та системи), 2025, Том 1, № 1 by Author "Гольцев, Олексій О."

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    Алгоритм обчислення подібності між гістограмами для сегментації текстури
    (Інститут інформаційних технологій та систем НАН України, Видавничий дім "Академперіодика" НАН України, 2025) Гольцев, Олексій О.; Гольцев, Олександр Дмитрович; Суровцев, Ігор Вікторович
    Introduction. An algorithm for calculating the similarity degree between multidimensional histograms is presented. The proposed algorithm was intended for texture segmentation of images using histograms as texture features. The need to develop such a special algorithm is justified by the fact that the methods for estimating the similarity/difference measure between multidimensional vectors described in the literature provide such measures that are not very suitable for solving the texture segmentation task. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram components, but also takes into account their nearest neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The effectiveness of the histogram comparison algorithm was indirectly confirmed by the results of texture segmentation of the image segmentation model in experiments on processing various images, including natural landscapes. Methods. The task of calculating the similarity between histograms is considered. A special algorithm is proposed because the analogical methods described in the literature are not very suitable for solving the texture segmentation task. The main peculiarity of the algorithm is that it takes into account as the corresponding histogram components as their nearest neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The algorithm was implemented as a computer program. The effectiveness of the algorithm is indirectly confirmed by the results of texture segmentation of the image segmentation model in experiments on processing various images, including natural landscapes. Purpose. The goal of this work is to develop an efficient algorithm for assessing the similarity of histograms, such as brightness histograms and orientation histograms of the texture windows. The algorithm is based on the idea of taking into account not only the corresponding components of both histograms, but also the components of their immediate environment. Results. The main advantage of the proposed algorithm, compared to popular methods of calculating similarity/difference between objects (vectors), is that the range of similarity between the compared histograms (from complete similarity to complete difference) is 100%, while popular methods can offer several times smaller ranges of similarity percentage. Conclusion. The proposed algorithm provides a wide range of similarity between the compared histograms which is 100% (from complete similarity to complete difference), while popular methods can offer several times smaller ranges of similarity percentage. The algorithm was implemented as a computer program as a component of a model that solves the problem of segmenting a visual image into homogeneous texture areas. It is worth noting that the proposed histogram comparison algorithm calculates the similarity measure between histograms very quickly, since it uses only simple operations. The effectiveness of the algorithm for texture segmentation of images into homogeneous texture areas is confirmed by the results in the experiments on natural image processing. The results obtained in the experiments demonstrate the effectiveness of the algorithm and show that the algorithm performs correct (from a human point of view) texture segmentation of a wide range of images. Thus, the effectiveness of the key operation of the segmentation algorithm, the histogram comparison algorithm, is indirectly confirmed.