How Many Neighbours for Known-Item Search?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10433620" target="_blank" >RIV/00216208:11320/21:10433620 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-89657-7_5" target="_blank" >https://doi.org/10.1007/978-3-030-89657-7_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_5" target="_blank" >10.1007/978-3-030-89657-7_5</a>
Alternative languages
Result language
angličtina
Original language name
How Many Neighbours for Known-Item Search?
Original language description
In the ongoing multimedia age, search needs become more variable and challenging to aid. In the area of content-based similarity search, asking search engines for one or just a few nearest neighbours to a query does not have to be sufficient to accomplish a challenging search task. In this work, we investigate a task type where users search for one particular multimedia object in a large database. Complexity of the task is empirically demonstrated with a set of experiments and the need for a larger number of nearest neighbours is discussed. A baseline approach for finding a larger number of approximate nearest neighbours is tested, showing potential speed-up with respect to a naive sequential scan. Last but not least, an open efficiency challenge for metric access methods is discussed for datasets used in the experiments.
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/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexible models for known-item search in large video collections</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
SISAP 2021: Similarity Search and Applications
ISBN
978-3-030-89657-7
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
54-65
Publisher name
Springer
Place of publication
Cham
Event location
Dortmund, Germany
Event date
Sep 29, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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