Mining recurring events through forest growing

Junsong Yuan*, Jingjing Meng, Ying Wu, Jiebo Luo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Recurring events are short temporal patterns that consist of multiple instances in the target database. Without any a priori knowledge of the recurring events, in terms of their lengths, temporal locations, the total number of such events, and possible variations, it is a challenging problem to discover them because of the enormous computational cost involved in analyzing huge databases and the difficulty in accommodating all the possible variations without even knowing the target. We translate the recurring event mining problem into finding temporally continuous paths in a matching-trellis. A novel algorithm that simulates a "forest-growing" procedure in the matching-trellis is proposed. Each tree branch In the resulting forest naturally corresponds to a discovered repetition, with temporal and content variations tolerated. By using locality sensitive hashing (LSH) to find best matches efficiently, the overall complexity of our algorithm is only sub-quadratic to the size of the database. Experimental results on the TRECVID video data of 10.5 hours and a human dance video dataset of 32,260 frames show that our method can effectively and efficiently discover recurring events such as TV commercials from news videos and typical dance moves from human dance sequences, in spite of large temporal and content variations.

Original languageEnglish (US)
Article number4630766
Pages (from-to)1597-1607
Number of pages11
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number11
StatePublished - Nov 2008


  • Event mining
  • Motion pattern discovery
  • Temporal pattern discovery

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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