Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models

  • Authors:

  • Lexing Xie;Shih-Fu Chang;A. Divakaran;Huifang Sun

  • Affiliations:

  • Dept. of Electr. Eng., Columbia Univ., New York, NY, USA;Dept. of Electr. Eng., Columbia Univ., New York, NY, USA;Ricoh California Res. Center, Menlo Park, CA, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

  • Venue:
  • ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
  • Year:
  • 2003

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Abstract

Structure elements in a time sequence (e.g. video) are repetitive segments with consistent deterministic or stochastic characteristics. While most existing work in detecting structures follows a supervised paradigm, we propose a fully unsupervised statistical solution in this paper. We present a unified approach to structure discovery from long video sequences as simultaneously finding the statistical descriptions of structure and locating segments that matches the descriptions. We model the multilevel statistical structure as hierarchical hidden Markov models, and present efficient algorithms for learning both the parameters and the model structure. When tested on a specific domain, soccer video, the unsupervised learning scheme achieves very promising results: it automatically discovers the statistical descriptions of high-level structures, and at the same time achieves even slightly better accuracy in detecting discovered structures in unlabelled videos than a supervised approach designed with domain knowledge and trained with comparable hidden Markov models.