The study on large scale image processing architecture based on Hadoop2.0 clusters

Bongjin Oh, Jongyoul Park, Sunggeun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper describes the DeepView platform which is a pilot system to classify large scaled images collected from a VMS server based on Hadoop 2.0 clusters. Multiple DeepView Classifier tasks analyze images simultaneously to detect objects in the images. Classifier task is implemented as a direct YARN task instead of MapReduce task to avoid intensive disk I/O and limited input format. Moreover, the small data access problem of Hadoop can be avoided because Application Master controls YARN tasks to access only local blocks of image files before image classification starts.

Original languageEnglish
Title of host publication5th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2015
EditorsJose Maria Flores-Arias, Stefan Mozar, Dietmar Hepper, Milan Z. Bjelica, Hans L. Cycon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages474-475
Number of pages2
ISBN (Electronic)9781479987481
DOIs
StatePublished - 25 Jan 2016
Event5th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2015 - Berlin, Germany
Duration: 6 Sep 20159 Sep 2015

Publication series

Name5th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2015

Conference

Conference5th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2015
Country/TerritoryGermany
CityBerlin
Period6/09/159/09/15

Keywords

  • Bigdata platform
  • Hadoop 2.0
  • Large scale image processing
  • VMS
  • YARN

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