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Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang Sun. Learning Hierarchical Hidden Markov Models for Video Structure Discovery. Advent Technical Report Columbia University, December 2002.

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Structure elements in a time sequence are repetitive segments that bear consistent deterministic or stochastic characteristics. While most existing work in detecting structures follow 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, as well as the model structure including the complexity of each structure element and the number of elements in the stream. We have also proposed feature selection algorithms that iterate between a wrapper and a filter method to partition the large feature pool into consistent and compact subsets, upon which the hierarchical hidden Markov model is learned. When tested on a specific domain, soccer video, the unsupervised learning scheme achieves very promising results: the automatically selected feature set includes the manually identified intuitively most significant feature, and the system 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. (PDF 271K)


Lexing Xie
Shih-Fu Chang

BibTex Reference

   Author = {Xie, Lexing and Chang, Shih-Fu and Divakaran, Ajay and Sun, Huifang},
   Title = {Learning Hierarchical Hidden Markov Models for Video Structure Discovery},
   Institution = {Columbia University},
   Month = {December},
   Year = {2002}

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