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Video Mining with Frequent Itemset Configurations

  • Till Quack
  • Vittorio Ferrari
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)

Abstract

We present a method for mining frequently occurring objects and scenes from videos. Object candidates are detected by finding recurring spatial arrangements of affine covariant regions. Our mining method is based on the class of frequent itemset mining algorithms, which have proven their efficiency in other domains, but have not been applied to video mining before. In this work we show how to express vector-quantized features and their spatial relations as itemsets. Furthermore, a fast motion segmentation method is introduced as an attention filter for the mining algorithm. Results are shown on real world data consisting of music video clips.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Till Quack
    • 1
  • Vittorio Ferrari
    • 2
  • Luc Van Gool
    • 1
  1. 1.ETH Zurich 
  2. 2.LEAR – INRIA Grenoble 

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