| Texto completo | |
| Autor(es): Mostrar menos - |
Malaquias Junior, Roseval Donisete
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Bueno, Renato
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Banissi, E
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Ursyn, A
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Bannatyne, MWM
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Pires, JM
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Datia, N
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Huang, ML
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Huang, W
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Nguyen, QV
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Nazemi, K
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Kovalerchuk, B
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Nakayama, M
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Counsell, J
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Agapiou, A
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Khosrow-Shahi, F
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Chau, HW
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Li, M
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Laing, R
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Bouali, F
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Venturini, G
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Temperini, M
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Sarfraz, M
Número total de Autores: 23
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| Tipo de documento: | Artigo Científico |
| Fonte: | 2021 25TH INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV): AI & VISUAL ANALYTICS & DATA SCIENCE; v. N/A, p. 6-pg., 2021-01-01. |
| Resumo | |
In image databases, queries are usually carried out by comparing the similarity of features extracted from the images, such as texture, shape and color in order to find the images most similar to the defined query center. However, we propose in this work the visual analysis of trajectory-based queries, where instead of defining a single image as the query center, a set of images that represent different temporal instances ("query trajectory") is defined, retrieving the trajectories belonging to the delimited search area surrounding this query trajectory. This work proposes techniques for visualization of complex data trajectories, considering similarity. The attribution of visual context in the visualization of these trajectories may help in the perception of knowledge in the hidden structures of the data. We developed techniques to summarize related trajectories of classified data and rendering options to improve the visual context of the query in a virtual reality visualization environment. (AU) | |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |