Reverse image search for scientific data within and beyond the visible spectrum.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorAraujo, Flavio Henrique Duarte de-
Autor(es): dc.creatorSilva, Romuere Rodrigues Veloso e-
Autor(es): dc.creatorMedeiros, Fátima Nelsizeuma Sombra de-
Autor(es): dc.creatorParkinson, Dilworth Dula-
Autor(es): dc.creatorHexemer, Alexander-
Autor(es): dc.creatorCarneiro, Cláudia Martins-
Autor(es): dc.creatorUshizima, Daniela Mayumi-
Data de aceite: dc.date.accessioned2025-08-21T15:32:52Z-
Data de disponibilização: dc.date.available2025-08-21T15:32:52Z-
Data de envio: dc.date.issued2019-03-27-
Data de envio: dc.date.issued2019-03-27-
Data de envio: dc.date.issued2018-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/10837-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0957417418302987?via%3Dihub#!-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1017996-
Descrição: dc.descriptionThe explosion in the rate, quality and diversity of image acquisition instruments has propelled the de- velopment of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper intro- duces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR , a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectContent-based image retrieval-
Palavras-chave: dc.subjectConvolutional neural network-
Palavras-chave: dc.subjectScientific image recommendation-
Título: dc.titleReverse image search for scientific data within and beyond the visible spectrum.-
Aparece nas coleções:Repositório Institucional - UFOP

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