Efficient Extraction of Feature Signatures Using Multi-GPU Architecture
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139388" target="_blank" >RIV/00216208:11320/13:10139388 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-35728-2" target="_blank" >http://dx.doi.org/10.1007/978-3-642-35728-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-35728-2" target="_blank" >10.1007/978-3-642-35728-2</a>
Alternative languages
Result language
angličtina
Original language name
Efficient Extraction of Feature Signatures Using Multi-GPU Architecture
Original language description
Recent popular applications like online video analysis or image exploration techniques utilizing content-based retrieval create a serious demand for fast and scalable feature extraction implementations. One of the promising content-based retrieval modelsis based on the feature signatures and the signature quadratic form distance. Although the model proved its competitiveness in terms of the effectiveness, the slow feature extraction comprising costly k-means clustering limits the model only for preprocessing steps. In this paper, we present a highly efficient multi-GPU implementation of the feature extraction process, reaching more than two orders of magnitude speedup with respect to classical CPU platform and the peak throughput that exceeds $8$~thousand signatures per second. Such an implementation allows to extract requested batches of frames or images online without annoying delays. Moreover, besides online extraction tasks, our GPU implementation can be used also in a traditional
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP202%2F11%2F0968" target="_blank" >GAP202/11/0968: Large-scale Nonmetric Similarity Search in Complex Domains</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
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 Multimedia Modeling
ISBN
978-3-642-35727-5
ISSN
0302-9743
e-ISSN
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Number of pages
11
Pages from-to
446-456
Publisher name
Springer Heidelberg Dordrecht
Place of publication
London, New York
Event location
Huangshan, China
Event date
Jan 7, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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