Towards augmented database schemes by discovery of latent visual attributes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10396528" target="_blank" >RIV/00216208:11320/19:10396528 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5441/002/edbt.2019.83" target="_blank" >https://doi.org/10.5441/002/edbt.2019.83</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5441/002/edbt.2019.83" target="_blank" >10.5441/002/edbt.2019.83</a>
Alternative languages
Result language
angličtina
Original language name
Towards augmented database schemes by discovery of latent visual attributes
Original language description
When searching for complex data entities, such as products in an e-shop, relational attributes are used as filters within structured queries. However, in many domains the visual appearance of an item is important for a user, while coverage of visual appearance by relational attributes is left to database designer at design time and is by nature an incomplete and imperfect representation of the entity. Recent advances in computer vision, dominated by deep convolutional neural networks (DCNNs), are a promising tool to cover the gaps. It has been shown that activations of neurons of DCNNs correspond to understandable visual-semantic features of an input image. We envision that activations of neurons are of great use for search queries in domains with strong visual information, even when obtained from DCNNs models pre-trained on general imagery. Locally scoped visual features obtained using them can be combined to form search masks which would correlate to what humans understand as an attribute, when applied on the entire dataset. Ultimately, combination of visual features can be identified automatically and formed into immediate suggestion of a new relational attribute, leaving one last task for humans to turn this into augmentation of the database schema - putting a label on it.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-22224S" target="_blank" >GA17-22224S: User preference analytics in multimedia exploration models</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Database Technology — EDBT 2019 Proceedings of the 22nd International Conference on Extending Database Technology Lisbon, Portugal, March 26–29, 2019
ISBN
978-3-89318-081-3
ISSN
2367-2005
e-ISSN
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Number of pages
4
Pages from-to
670-673
Publisher name
OpenProceedings
Place of publication
Venice, Italy
Event location
Lisbon, Portugal
Event date
Mar 26, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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