Recursive estimation of generative models of video

Nemanja Petrovic, Aleksandar Ivanovic, Nebojsa Jojic, Sumit Basu, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present a generative model and learning procedure for unsupervised video clustering into scenes. The work addresses two important problems: realistic modeling of the sources of variability in the video and fast transformation invariant frame clustering. We suggest a solution to the problem of computationally intensive learning in this model by combining the recursive model estimation, fast inference, and on-line learning. Thus, we achieve real time frame clustering performance. Novel aspects of this method include an algorithm for the clustering of Gaussian mixtures, and the fast computation of the KL divergence between two mixtures of Gaussians. The efficiency and the performance of clustering and KL approximation methods are demonstrated. We also present novel video browsing tool based on the visualization of the variables in the generative model.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
PublisherIEEE Computer Society
Pages79-86
Number of pages8
ISBN (Print)0769525970, 9780769525976
DOIs
StatePublished - 2006
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
CountryUnited States
CityNew York, NY
Period6/17/066/22/06

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Petrovic, N., Ivanovic, A., Jojic, N., Basu, S., & Huang, T. (2006). Recursive estimation of generative models of video. In Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 (pp. 79-86). [1640744] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/CVPR.2006.248