Проблема автоматичної класифікації зображень за використання інтелектуальної системи прийняття рішень на основі графа знань і точного аналізу зображень

In order to prevent the illegal export of paintings abroad, a museum examination using various methods for studying a work of art is carried out. At the same time, an analysis is also made of historical, art history, financial and other information and documents confirming the painting’s authenticit...

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Збережено в:
Бібліографічні деталі
Дата:2022
Автори: Martynenko, Andrii, Tevyashev, Andriy, Kulishova, Nonna, Moroz, Boris
Формат: Стаття
Мова:English
Опубліковано: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2022
Теми:
Онлайн доступ:http://journal.iasa.kpi.ua/article/view/275082
Теги: Додати тег
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Назва журналу:System research and information technologies

Репозитарії

System research and information technologies
Опис
Резюме:In order to prevent the illegal export of paintings abroad, a museum examination using various methods for studying a work of art is carried out. At the same time, an analysis is also made of historical, art history, financial and other information and documents confirming the painting’s authenticity — provenance. Automation of such examination is hampered by the need to take into account numerical values of visual features, quality indicators, and verbal descriptions from provenance. In this paper, we consider the problem of automatic multi-task classification of paintings for museum expertise. A system architecture is proposed that checks provenance, implements a fine-grained image analysis (FGIA) of visual image features, and automatically classifies a painting by authorship, genre, and time of creation. Provenance is contained in a knowledge graph; for its vectorization, it is proposed to use a graph2vec type encoder with an attention mechanism. Fine-grained image analysis is proposed to be performed using searching discriminative regions (SDR) and learning discriminative regions (LDR) allocated by convolutional neural networks. To train the classifier, a generalized loss function is proposed. A data set is also proposed, including provenance and images of paintings by European and Ukrainian artists.