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.
Download paper:
Adobe portable document (pdf) 
Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
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)
@TechReport{dvmmPub240,
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}
}
Get EndNote Reference (.ref)
For problems or questions regarding this web site contact The
Web Master.
This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).
