PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Video Mining with Frequent Itemset Configurations
Till Quack, Vittorio Ferrari and Luc van Gool
In: CIVR 2006, Arizona, USA(2006).


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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2304
Deposited By:Vittorio Ferrari
Deposited On:11 November 2006