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The MASH Project

  • François Fleuret
  • Philip Abbet
  • Charles Dubout
  • Leonidas Lefakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)

Abstract

It has been demonstrated repeatedly that combining multiple types of image features improves the performance of learning-based classification and regression. However, no tools exist to facilitate the creation of large pools of feature extractors by extended teams of contributors.

The MASH project aims at creating such tools. It is organized around the development of a collaborative web platform where participants can contribute feature extractors, browse a repository of existing ones, run image classification and goal-planning experiments, and participate in public large-scale experiments and contests.

The tools provided on the platform facilitate the analysis of experimental results. In particular, they rank the feature extractors according to their efficiency, and help to identify the failure mode of the prediction system.

Keywords

pattern recognition image features collaborative design 

References

  1. 1.
    Toscher, A., Jahrer, M., Bell, R.: The bigchaos solution to the netflix grand prize (2009), http://www.netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf
  2. 2.
    Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: International Conference on Computer Vision (2009)Google Scholar
  3. 3.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • François Fleuret
    • 1
  • Philip Abbet
    • 1
  • Charles Dubout
    • 1
  • Leonidas Lefakis
    • 1
  1. 1.Idiap Research InstituteMartignySwitzerland

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