TY - GEN
T1 - Clustering dynamic textures with the hierarchical EM algorithm
AU - Chan, Antoni B.
AU - Coviello, Emanuele
AU - Lanckriet, Gert R. G.
PY - 2010
Y1 - 2010
N2 - 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-of-systems codebook generation. ©2010 IEEE.
AB - 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-of-systems codebook generation. ©2010 IEEE.
UR - https://www.scopus.com/pages/publications/77956006220
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77956006220&origin=recordpage
U2 - 10.1109/CVPR.2010.5539878
DO - 10.1109/CVPR.2010.5539878
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424469840
SP - 2022
EP - 2029
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PB - IEEE
T2 - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010)
Y2 - 13 June 2010 through 18 June 2010
ER -