Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10294741" target="_blank" >RIV/00216208:11320/15:10294741 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-015-2726-y" target="_blank" >http://dx.doi.org/10.1007/s11042-015-2726-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-015-2726-y" target="_blank" >10.1007/s11042-015-2726-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures

Popis výsledku v původním jazyce

Similarity search and content-based retrieval have become widely used in multimedia database systems that often manage huge data collections. Unfortunately, many effective content-based similarity models cannot be fully utilized for larger datasets, as they are computationally demanding and require massive parallel processing for both feature extraction and query evaluation tasks. In this work, we address the performance issues of effective similarity models based on feature signatures, where we focus on fast feature extraction from image thumbnails using affordable hardware. More specifically, we propose a multi-GPU implementation that increases the extraction speed by two orders of magnitude with respect to a~single-threaded CPU implementation. Sincethe extraction algorithm is not directly parallelizable, we propose a modification of the algorithm embracing the SIMT execution model. We have experimentally verified that our GPU extractor can be successfully used to index large image

Název v anglickém jazyce

Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures

Popis výsledku anglicky

Similarity search and content-based retrieval have become widely used in multimedia database systems that often manage huge data collections. Unfortunately, many effective content-based similarity models cannot be fully utilized for larger datasets, as they are computationally demanding and require massive parallel processing for both feature extraction and query evaluation tasks. In this work, we address the performance issues of effective similarity models based on feature signatures, where we focus on fast feature extraction from image thumbnails using affordable hardware. More specifically, we propose a multi-GPU implementation that increases the extraction speed by two orders of magnitude with respect to a~single-threaded CPU implementation. Sincethe extraction algorithm is not directly parallelizable, we propose a modification of the algorithm embracing the SIMT execution model. We have experimentally verified that our GPU extractor can be successfully used to index large image

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Svazek periodika

2015

Číslo periodika v rámci svazku

27.6.2015

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

33

Strana od-do

1-33

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-84933574095