• Niels Haering
  • Niels Da Vitoria Lobo
Part of the The International Series in Video Computing book series (VICO, volume 2)


In this chapter we show the results of our comparison of the linear, the quadratic, the convolutional neural networ, and the back-propagation neural network and demonstrate a method to extract powerful subsets of the features used to describe still images and video frames. Good feature sets can be found that preserve much of the robustness of the entire feature set using only about a quarter of all features. Sections 3 and 4 in this chapter show the performance of the various intermediate components of the framework as well as the final classification and event detection results.


Greedy Algorithm Neural Network Classifier Shot Boundary Shot Boundary Detection Rocket Launch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Niels Haering
    • 1
  • Niels Da Vitoria Lobo
    • 2
  1. 1.DiamondBack Vision, IncRestonUSA
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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