[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Part of the book series: Studies in Computational Intelligence ((SCI,volume 384))

  • 504 Accesses

Abstract

Within the context of detection of incongruent events, an often overlooked aspect is how a system should react to the detection. The set of all the possible actions is certainly conditioned by the task at hand, and by the embodiment of the artificial cognitive system under consideration. Still, we argue that a desirable action that does not depend from these factors is to update the internal model and learn the new detected event. This paper proposes a recent transfer learning algorithm as the way to address this issue. A notable feature of the proposed model is its capability to learn from small samples, even a single one. This is very desirable in this context, as we cannot expect to have too many samples to learn from, given the very nature of incongruent events.We also show that one of the internal parameters of the algorithm makes it possible to quantitatively measure incongruence of detected events. Experiments on two different datasets support our claim.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Cawley, G.C.: Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In: IJCNN (2006)

    Google Scholar 

  2. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  3. Gehler, P., Nowozin, S.: Let the kernel figure it out: Principled learning of pre-processing for kernel classifiers. In: Proc. CVPR (2009)

    Google Scholar 

  4. Griffin, G., Holub, A., Perona, P.: Caltech 256 object category dataset. Technical Report UCB/CSD-04-1366, California Institue of Technology (2007)

    Google Scholar 

  5. Nater, F., Grabner, H., van Gool, L.: Exploiting simple hierarchies for unsupervised human behavior analysis. In: Proc. CVPR (2010)

    Google Scholar 

  6. Tommasi, T., Orabona, F., Caputo, B.: Safety in numbers: Learning categories from few examples with multi model knowledge transfer. In: Proc. CVPR (2010)

    Google Scholar 

  7. Weinshall, D., Hermansky, H., Zweig, A., Luo, J., Brgge Jimison, H., Ohl, F., Pavel, M.: Beyond novelty detection: Incongruent events, when general and specific classifiers disagree. In: Proc. NIPS (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tommasi, T., Caputo, B. (2012). Towards a Quantitative Measure of Rareness. In: Weinshall, D., Anemüller, J., van Gool, L. (eds) Detection and Identification of Rare Audiovisual Cues. Studies in Computational Intelligence, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24034-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24034-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24033-1

  • Online ISBN: 978-3-642-24034-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics