Practical scalable image analysis and indexing using Hadoop


Hare, Jonathon S., Samangooei, Sina and Lewis, Paul H. (2012) Practical scalable image analysis and indexing using Hadoop. Multimedia Tools and Applications, 1-34. (doi:10.1007/s11042-012-1256-0).

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Description/Abstract

The ability to handle very large amounts of image data is important for image analysis, indexing and retrieval applications. Sadly, in the literature, scalability aspects are often ignored or glanced over, especially with respect to the intricacies of actual implementation details.

In this paper we present a case-study showing how a standard bag-of-visual-words image indexing pipeline can be scaled across a distributed cluster of machines. In order to achieve scalability, we investi- gate the optimal combination of hybridisations of the MapReduce distributed computational framework which allows the components of the analysis and indexing pipeline to be effectively mapped and run on modern server hardware. We then demonstrate the scalability of the approach practically with a set of image analysis and indexing tools built on top of the Apache Hadoop MapReduce framework. The tools used for our experiments are freely available as open-source software, and the paper fully describes the nuances of their implementation.

Item Type: Article
ISSNs: 1380-7501 (print)
1573-7721 (electronic)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Web & Internet Science
ePrint ID: 344243
Date Deposited: 12 Nov 2012 12:10
Last Modified: 27 Mar 2014 20:26
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/344243

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