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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, pp. 1-34. (doi:10.1007/s11042-012-1256-0).

Record type: Article


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.

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Published date: 6 November 2012
Organisations: Web & Internet Science


Local EPrints ID: 344243
ISSN: 1380-7501
PURE UUID: 8cf2c3b4-5d82-4543-bdec-8003707046cb
ORCID for Jonathon S. Hare: ORCID iD

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Date deposited: 12 Nov 2012 12:10
Last modified: 18 Jul 2017 05:19

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Author: Sina Samangooei
Author: Paul H. Lewis

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