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A Markov Clustering Topic Model for mining behaviour in video
2009
2009 IEEE 12th International Conference on Computer Vision
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robustness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into
doi:10.1109/iccv.2009.5459342
dblp:conf/iccv/HospedalesGX09
fatcat:aqswyqrgcngdtkkfmw3d7h24wy