Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementat...

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Main Authors:	A. Asilian Bidgoli, H. Ebrahimpour-Komle, M. Askari, Seyed J. Mousavirad
Format:	Article
Language:	English
Published:	Shahrood University of Technology 2019-03-01
Series:	Journal of Artificial Intelligence and Data Mining
Subjects:	Object recognition Spatial pyramid match kernel Parallel computing Cluster of computers Support Vector Machine
Online Access:	http://jad.shahroodut.ac.ir/article_1159_169528cafc621bc8706b75fb43a5d686.pdf

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spelling	doaj-a9c94d9e7aac49d19741500f6b494e9e2020-11-24T21:00:18ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442019-03-01719710810.22044/jadm.2017.4152.15031159Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of ComputersA. Asilian Bidgoli0H. Ebrahimpour-Komle1M. Askari2Seyed J. Mousavirad31) Department of Computer Engineering, University of Kashan, Kashan, Iran 2)Instructor, Faculty of Elecronic and Computer Engineering Pooyesh Higher Education Institute Qom, IranDepartment of Computer Engineering, University of Kashan, Kashan, IranDepartment of Computer Engineering, University of Kashan, Kashan, IranDepartment of Computer Engineering, University of Kashan, Kashan, IranThis paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors.http://jad.shahroodut.ac.ir/article_1159_169528cafc621bc8706b75fb43a5d686.pdfObject recognitionSpatial pyramid match kernelParallel computingCluster of computersSupport Vector Machine
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	A. Asilian Bidgoli H. Ebrahimpour-Komle M. Askari Seyed J. Mousavirad
spellingShingle	A. Asilian Bidgoli H. Ebrahimpour-Komle M. Askari Seyed J. Mousavirad Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers Journal of Artificial Intelligence and Data Mining Object recognition Spatial pyramid match kernel Parallel computing Cluster of computers Support Vector Machine
author_facet	A. Asilian Bidgoli H. Ebrahimpour-Komle M. Askari Seyed J. Mousavirad
author_sort	A. Asilian Bidgoli
title	Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
title_short	Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
title_full	Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
title_fullStr	Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
title_full_unstemmed	Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers
title_sort	parallel spatial pyramid match kernel algorithm for object recognition using a cluster of computers
publisher	Shahrood University of Technology
series	Journal of Artificial Intelligence and Data Mining
issn	2322-5211 2322-4444
publishDate	2019-03-01
description	This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors.
topic	Object recognition Spatial pyramid match kernel Parallel computing Cluster of computers Support Vector Machine
url	http://jad.shahroodut.ac.ir/article_1159_169528cafc621bc8706b75fb43a5d686.pdf
work_keys_str_mv	AT aasilianbidgoli parallelspatialpyramidmatchkernelalgorithmforobjectrecognitionusingaclusterofcomputers AT hebrahimpourkomle parallelspatialpyramidmatchkernelalgorithmforobjectrecognitionusingaclusterofcomputers AT maskari parallelspatialpyramidmatchkernelalgorithmforobjectrecognitionusingaclusterofcomputers AT seyedjmousavirad parallelspatialpyramidmatchkernelalgorithmforobjectrecognitionusingaclusterofcomputers
_version_	1716780263453753344

Parallel Spatial Pyramid Match Kernel Algorithm for Object Recognition using a Cluster of Computers

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