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Video Mining with Frequent Itemset Configurations

T. Quack, V. Ferrari and L. Van Gool
5th International Conference on Image and Video Retrieval, CIVR 2006, Phoenix, July 13-15, 2006
July 2006

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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@InProceedings{eth_biwi_00431,
  author = {T. Quack and V. Ferrari and L. Van Gool},
  title = {Video Mining with Frequent Itemset Configurations},
  booktitle = {5th International Conference on Image and Video Retrieval, CIVR 2006, Phoenix, July 13-15, 2006},
  year = {2006},
  month = {July},
  keywords = {video mining}
}