Skip to main content
SpringerLink
Log in

Multimedia Big Data: Content Analysis and Retrieval

  • Chapter
  • First Online:
Big-Data Analytics and Cloud Computing

Abstract

This chapter surveys recent developments in the area of multimedia big data, the biggest big data. One core problem is how to best process this multimedia big data in an efficient and scalable way. We outline examples of the use of the MapReduce framework, including Hadoop, which has become the most common approach to a truly scalable and efficient framework for common multimedia processing tasks, e.g., content analysis and retrieval. We also examine recent developments on deep learning which has produced promising results in large-scale multimedia processing and retrieval. Overall the focus has been on empirical studies rather than the theoretical so as to highlight the most practically successful recent developments and highlight the associated caveats or lessons learned.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. (2015) Special issue on multimedia: the biggest big data. IEEE Trans Multimed 17(1):144

    Google Scholar 

  2. Smith JR (2013) Riding the multimedia big data wave. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’13), Dublin, 28 July–01 Aug, pp 1–2

    Google Scholar 

  3. Video correlation and analysis suite from IBM. http://www.ibm.com/smarterplanet/ie/en/smarter_cities/solutions/solution/A863656S12083Q41.html

  4. Chang EY (2011) Foundations of large-scale multimedia information management and retrieval: mathematics of perception. Springer, New York

    Book  MATH  Google Scholar 

  5. McFee B, Lanckriet G (2011) Learning multi-modal similarity. J Mach Learn Res 12:491–523

    MathSciNet  MATH  Google Scholar 

  6. Burnett I, Van de Walle R, Hill K, Bormans J, Pereira F (2003) MPEG-21: goals and achievements. IEEE MultiMed 10(4):60–70

    Article  Google Scholar 

  7. Gurrin C, Smeaton AF, Doherty AR (2014) LifeLogging: personal big data. Found Trends Inf Retr 8(1):1–125

    Article  Google Scholar 

  8. Moise D, Shestakov D, Gudmundsson G, Amsaleg L (2013) Indexing and searching 100M images with map-reduce. In: Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval (ICMR ’13), Dallas, 16–19 Apr, pp 17–24

    Google Scholar 

  9. Krishna M, Kannan B, Ramani A, Sathish SJ (2010) Implementation and performance evaluation of a hybrid distributed system for storing and processing images from the web. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), Indianapolis, 30 Nov-03 Dec, pp 762–767

    Google Scholar 

  10. Meeker M (2014) Internet Trends 2014 – Code Conference

    Google Scholar 

  11. Chen SY, Lai CF, Hwang RH, Chao HC, Huang YM (2014) A multimedia parallel processing approach on GPU MapReduce framework. In: Proceedings of the 7th International Conference on Ubi-Media Computing and Workshops (UMEDIA), Ulaanbaatar, 12–14 July, pp 154–159

    Google Scholar 

  12. He B, Fang W, Luo Q, Govindaraju NK, Wang T (2008) Mars: a MapReduce framework on graphics processors. In: Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques (PACT’08), Toronto, 25–29 Oct, pp 260–269

    Google Scholar 

  13. Wang H, Shen Y, Wang L, Zhufeng K, Wang W, Cheng C (2012) Large-scale multimedia data mining using MapReduce framework. In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom’12), Taipei, 3–6 Dec, pp 287–292

    Google Scholar 

  14. Mera D, Batko M, Zezula P (2014) Towards fast multimedia feature extraction: Hadoop or storm. In: IEEE International Symposium on Multimedia (ISM’14), Taichung, 10–12 Dec, pp 106–109

    Google Scholar 

  15. Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process 3:e2

    Article  Google Scholar 

  16. Cadieu CF, Hong H, Yamins DLK, Pinto N, Ardila D et al (2014) Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput Biol 10(12):e1003963

    Article  Google Scholar 

  17. Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS’12), Lake Tahoe, Nevada

    Google Scholar 

  19. Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  20. Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525

    Article  Google Scholar 

  21. Ciresan D, Giusti A, Gambardella L, Schidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS’12), Lake Tahoe, 03–08 Dec, pp 2852–2860

    Google Scholar 

  22. Zeiler M, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. CoRR, abs/1301.3557

    Google Scholar 

  23. Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the ACM international conference on multimedia (MM’14), Orlando. ACM, New York, pp 157–166

    Google Scholar 

  24. High-performance C++/CUDA implementation of convolutional neural networks. https://code.google.com/p/cuda-convnet/

  25. Hua J, Shao J, Tian H, Zhao Z, Su F, Cai A (2014) An output aggregation system for large scale cross-modal retrieval. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW’14), Chengdu, 14–18 July 2014, pp 1–6

    Google Scholar 

  26. MSR-Bing Image Retrieval Challenge. http://research.microsoft.com/en-US/projects/irc/acmmm2014.aspx

  27. Nair V, Hinton G (2009) 3-D object recognition with deep belief nets. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS’12), Lake Tahoe, 03–08 Dec, pp 1339–1347

    Google Scholar 

  28. ISBI 2012 EM Segmentation Challenge. http://brainiac2.mit.edu/isbi_challenge/

  29. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng A (2011) Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML11), Bellevue, USA, 28 June-02 July, pp 689–696

    Google Scholar 

  30. Coates A, Huval B, Wang T, Wu D, Ng A, Catanzaro B (2013) Deep learning with COTS HPC systems. In: Proceedings of the 30th International Conference on Machine Learning (ICML13), Atlanta, 16–21 June, pp 1337–1345

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM Research, Dublin, Ireland

    Jer Hayes

Authors
  1. Jer Hayes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jer Hayes .

Editor information

Editors and Affiliations

  1. University of Derby, Derby, United Kingdom

    Marcello Trovati

  2. University of Derby, Derby, United Kingdom

    Richard Hill

  3. University of Derby, Derby, United Kingdom

    Ashiq Anjum

  4. University of Derby, Derby, United Kingdom

    Shao Ying Zhu

  5. University of Derby, Derby, United Kingdom

    Lu Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hayes, J. (2015). Multimedia Big Data: Content Analysis and Retrieval. In: Trovati, M., Hill, R., Anjum, A., Zhu, S., Liu, L. (eds) Big-Data Analytics and Cloud Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-25313-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25313-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25311-4

  • Online ISBN: 978-3-319-25313-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation