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Clustering Dynamic Textures with the Hierarchical EM Algorithm

10 years 7 months ago
Clustering Dynamic Textures with the Hierarchical EM Algorithm
The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and video registration. In this paper, we derive a new algorithm for clustering DT models that is based on the hierarchical EM algorithm. The proposed clustering algorithm is capable of both clustering DTs and learning novel DT cluster centers that are representative of the cluster members, in a manner that is consistent with the underlying generative probabilistic model of the DT. We then demonstrate the efficacy of the clustering algorithm on several applications in motion analysis, including hierarchical motion clustering, semantic motion annotation, and bag-ofsystems codebook generation.
Antoni Chan, Emanuele Coviello, Gert Lanckriet
Added 07 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Antoni Chan, Emanuele Coviello, Gert Lanckriet
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