Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures

Original language description

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

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2015

Confidentiality

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

Name of the periodical

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

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Volume of the periodical

2015

Issue of the periodical within the volume

27.6.2015

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

33

Pages from-to

1-33

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-84933574095