Özet:

Abstract The work deals with issues of clustering sets of characteristic features of images. For the construction of array of the characteristic features is used method Speeded Up Robust Features. Implemented algorithms for clustering structural descriptions of images on the basis of a self-organizing Kohonen neural network and method of grouping the difference. The object of the research are clustering methods which applied to the set of structural features. The aim is to construct a vector representations of descriptions based on clustering, which increases the speed of recognition. The subject of research is systematization a set of structural features of visual objects. Discussing the results of the application of clustering methods for structural descriptions of images in the form of sets of characteristic features to improve the performance of visual recognition of objects. For systematization and compression the feature space proposed to carry out self-study using the methods of differential grouping and Kohonen networks. The simulation and experimental study of clustering methods on examples of specific sets of characteristic features were done. The research results proves the possibility of effective representation of the descriptions in the form of a vector with integer elements. This approach can be used to solve problems of recognition and retrieval of images. As a result compact vector description of etalon images is built, quantitative estimates of clustering error are estimated, efficiency of proposed method during processing of real image database is confirmed. References Гороховатский В. А. Структурный анализ и интеллектуальная обработка данных в компьютерном зрении / В. А. Гороховатский. – Харьков : Компания СМИТ, 2014. – 316 с. 2. Контурная обработка динамических изображений / [Л. И. Тимченко, А. А. Поплавский, Н. И. Кокряцкая и др.]. – Киев : Наукова думка, 2013. – 239 с. 3. Осовский С. Нейронные сети для обработки информации / С. Осовский. – М. : Финансы и статистика, 2002. – 344 с. 4. Bay H. Surf: Speeded up robust features / H. Bay, T. Tuytelaars, L.Van Gool // Computer Vision : Ninth European Conference on Computer Vision, Graz, 7–13 May, 2006: proceedings. – Berlin : Springer, 2006. – P.404–417. 5. Паклин Н. Б. Бизнес-аналитика: от данных к знаниям / Н. Б. Паклин, В. И. Орешков. – СПб. : Питер, 2013. – 704 с. 6. Duda R. O. Pattern classification. Second edition / R. O. Duda, P. E. Hart, D. G. Stork. – New York : Wiley, 2000. – 738 p. 7. Прикладная статистика: Классификация и снижение размерности / [С. А. Айвазян, В. М. Бухштабер, И. С. Енюков, Л. Д. Мешалкин; под ред. С. А. Айвазяна.]. – М. : Финансы и статистика, 1989. – 607 с. 8. Берестовский А. Е. Нейросетевые технологии самообучения в системах структурного распознавания визуальных объектов / А. Е. Берестовский, А. Н. Власенко, В. А. Гороховатский // Реєстрація, зберігання і обробка даних. – 2015. – № 1. – С. 108–120. 9. Кохонен Т. Самоорганизующиеся карты / Т. Кохонен. – М. : БИНОМ, Лаборатория знаний, 2013. – 655 с.

Anahtar Kelimeler: