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CIVR
2006
Springer

Video Mining with Frequent Itemset Configurations

13 years 7 months ago
Video Mining with Frequent Itemset Configurations
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 vectorquantized 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.
Till Quack, Vittorio Ferrari, Luc J. Van Gool
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where CIVR
Authors Till Quack, Vittorio Ferrari, Luc J. Van Gool
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