Semantic search of relevant images using vector databases

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dc.contributor.authorOstapchenko, N.
dc.contributor.authorTyschenko, O.
dc.contributor.authorDenysenko, B.
dc.contributor.authorMazurets, O.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2025-05-29T15:58:22Z
dc.date.available2025-05-29T15:58:22Z
dc.date.issued2025
dc.description.abstractThis paper presents a method for semantic image search using vector databases, enabling efficient retrieval of relevant visual content based on textual or visual queries. The proposed approach leverages state-of-the-art embedding models, such as OpenCLIP, to convert both images and queries into high-dimensional vector representations. These vectors are stored and compared in a vector database to determine semantic similarity. The system supports both text-to-image and image-to-image search, significantly enhancing the precision of results by focusing on content-level meaning rather than keywords. The solution is applicable in various domains requiring fast and accurate access to large-scale image repositories.
dc.identifier.citationOstapchenko N., Tyschenko O., Denysenko B., Mazurets O. Semantic search of relevant images using vector databases. Modern Scientific Research: Theoretical and Practical Aspects. Proceedings II International Scientific and Practical Conference. May 26-28, 2025. Riga, Latvia. Pp. 161-165
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18414
dc.language.isoen
dc.titleSemantic search of relevant images using vector databases
dc.typeСтаття
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