A hierarchical Dirichlet process mixture of GID Distributions with feature selection for spatio-temporal video modeling and segmentation

Wentao Fan; Nizar Bouguila; Xin Liu

ICASSP 2017accepted0 citations

A hierarchical Dirichlet process mixture of GID Distributions with feature selection for spatio-temporal video modeling and segmentation

Abstract

In this paper, a hierarchical Dirichlet process (HDP) mixture model of generalized inverted Dirichlet (GID) distributions with an unsupervised feature selection scheme is developed. The proposed model is learned via a principled variational framework and then deployed for video modeling and segmentation. Experimental results show the merits of our developed statistical framework.

BibTeX

@inproceedings{icassp2017_ahierarchicaldir,
  title = {A hierarchical Dirichlet process mixture of GID Distributions with feature selection for spatio-temporal video modeling and segmentation},
  author = {Wentao Fan and Nizar Bouguila and Xin Liu},
  booktitle = {ICASSP 2017},
  year = {2017}
}