About: Efficient Extraction of Feature Signatures Using Multi-GPU Architecture

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Efficient Extraction of Feature Signatures Using Multi-GPU Architecture Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 models is 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 preprocessing and training phase. For example, fast extraction allows indexing of huge databases or inspecting significantly larger parameter space when searching for an optimal similarity model configuration that is optimal according to both efficiency and effectiveness. 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 models is 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 preprocessing and training phase. For example, fast extraction allows indexing of huge databases or inspecting significantly larger parameter space when searching for an optimal similarity model configuration that is optimal according to both efficiency and effectiveness. (en)
Title	Efficient Extraction of Feature Signatures Using Multi-GPU Architecture Efficient Extraction of Feature Signatures Using Multi-GPU Architecture (en)
skos:prefLabel	Efficient Extraction of Feature Signatures Using Multi-GPU Architecture Efficient Extraction of Feature Signatures Using Multi-GPU Architecture (en)
skos:notation	RIV/00216208:11320/13:10139388!RIV14-GA0-11320___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(GAP202/11/0968), S
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Lokoč, Jakub Skopal, Tomáš Kruliš, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Karlova v Praze / Matematicko-fyzikální fakulta
http://linked.open...dnocenehoVysledku	72066
http://linked.open...ai/riv/idVysledku	RIV/00216208:11320/13:10139388
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	parallel; GPU; feature extraction; similarity search (en)
http://linked.open.../riv/klicoveSlovo	similarity search GPU feature extraction parallel
http://linked.open...ontrolniKodProRIV	[EC630F85348B]
http://linked.open...v/mistoKonaniAkce	Huangshan, China
http://linked.open...i/riv/mistoVydani	London, New York
http://linked.open...i/riv/nazevZdroje	Advances in Multimedia Modeling
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Large-scale Nonmetric Similarity Search in Complex Domains
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Lokoč, Jakub Skopal, Tomáš Kruliš, Martin
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-01-07 (xsd:date)
issn	0302-9743
number of pages	11 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-642-35728-2
http://purl.org/ne...btex#hasPublisher	Springer Heidelberg Dordrecht
https://schema.org/isbn	978-3-642-35727-5
http://localhost/t...ganizacniJednotka	11320

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software