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Trajectory-Based Video Retrieval Using Dirichlet Process Mixture Models
2008
Procedings of the British Machine Vision Conference 2008
In this paper, we present a trajectory-based video retrieval framework using Dirichlet process mixture models. The main contribution of this framework is four-fold. (1) We apply a Dirichlet process mixture model (DPMM) to unsupervised trajectory learning. DPMM is a countably infinite mixture model with its components growing by itself. (2) We employ a time-sensitive Dirichlet process mixture model (tDPMM) to learn trajectories' time-series characteristics. Furthermore, a novel likelihood
doi:10.5244/c.22.106
dblp:conf/bmvc/LiHZZL08
fatcat:ksbviopp6fflxp4wgg4yfe3fpe