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Practical scalable image analysis and indexing using Hadoop

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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 investigate 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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Notes

  1. http://www.flickr.com/photos/eon60/6000000000/

  2. http://photobucket.com/

  3. http://twitter.com/#!/facebook/status/22372857292005376

  4. http://nips.cc/Conferences/2011/Program/event.php?ID=2518

  5. http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=13548&copyownerid=19220

  6. http://openmp.org/wp/

  7. http://www.systemc.org/home/

  8. http://www.khronos.org/opencl/

  9. For more details on the big data GPU state of the art see this Azinta Systems blog post http://bit.ly/gpuMapReduce

  10. http://hadoop.apache.org/

  11. http://www.iterativeMapReduce.org/

  12. http://discoproject.org/

  13. http://www.mongodb.org/display/DOCS/MapReduce

  14. http://mahout.apache.org/

  15. https://cwiki.apache.org/MAHOUT/k-means-clustering.html

  16. Google has recently moved to a distributed in-memory inverted index for its web search engine, however this requires massive amounts of hardware [11, see].

  17. http://www.openimaj.org

  18. http://www.imageterrier.org

  19. http://www.terrier.org

  20. http://www.image-net.org

  21. https://github.com/sinjax/ssh-imagenet-timeingTest

  22. http://www.vis.uky.edu/~stewe/ukbench/

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Acknowledgements

The development of the tools and techniques described in this paper was funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreements n° 270239 (ARCOMEM), 231126 (LivingKnowledge) and 287863 (TrendMiner) together with the LiveMemories project, graciously funded by the Autonomous Province of Trento (Italy).

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  1. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Jonathon S. Hare, Sina Samangooei & Paul H. Lewis

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  1. Jonathon S. Hare
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  2. Sina Samangooei
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Correspondence to Sina Samangooei.

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Hare, J.S., Samangooei, S. & Lewis, P.H. Practical scalable image analysis and indexing using Hadoop. Multimed Tools Appl 71, 1215–1248 (2014). https://doi.org/10.1007/s11042-012-1256-0

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