| dc.rights.license | Visos teisės saugomos / All rights reserved | en_US |
| dc.contributor.author | Smelyakov, Kirill | |
| dc.contributor.author | Chupryna, Anastasiya | |
| dc.contributor.author | Sandrkin, Denys | |
| dc.contributor.author | Kolisnyk, Maksym | |
| dc.date.accessioned | 2025-12-15T11:40:23Z | |
| dc.date.available | 2025-12-15T11:40:23Z | |
| dc.date.issued | 2020 | |
| dc.identifier.isbn | 9781728197807 | en_US |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/159548 | |
| dc.description.abstract | The paper discusses promising ways to build an invariant model and an efficient image search algorithm in Big Image Warehouse. Based on open data using the appropriate Python prototype, Search by Image Engine, a series of experiments is conducted to search for images in Big Data Warehouses. Estimates of the effectiveness of the search using the developed prototype, taking into account valid image transforms are given. | en_US |
| dc.format.extent | 4 p. | en_US |
| dc.format.medium | Tekstas / Text | en_US |
| dc.language.iso | en | en_US |
| dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/159395 | en_US |
| dc.source.uri | https://ieeexplore.ieee.org/document/9108782 | en_US |
| dc.subject | image | en_US |
| dc.subject | search engine | en_US |
| dc.subject | big data | en_US |
| dc.subject | image warehouse | en_US |
| dc.subject | computational efficiency | en_US |
| dc.title | Search by Image Engine for Big Data Warehouse | en_US |
| dc.type | Konferencijos publikacija / Conference paper | en_US |
| dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
| dcterms.issued | 2020-06-05 | |
| dcterms.references | 19 | en_US |
| dc.description.version | Taip / Yes | en_US |
| dc.contributor.institution | Kharkiv National University of Radio electronics | en_US |
| dcterms.sourcetitle | 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 30, 2020, Vilnius, Lithuania | en_US |
| dc.identifier.eisbn | 9781728197791 | en_US |
| dc.publisher.name | IEEE | en_US |
| dc.publisher.country | United States of America | en_US |
| dc.publisher.city | New York | en_US |
| dc.identifier.doi | https://doi.org/10.1109/eStream50540.2020.9108782 | en_US |
