A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
On the Recognition of Abstract Markov Policies
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Topic transition detection using hierarchical hidden Markov and semi-Markov models
Proceedings of the 13th annual ACM international conference on Multimedia
A daily behavior enabled hidden Markov model for human behavior understanding
Pattern Recognition
A daily behavior enabled hidden Markov model for human behavior understanding
Pattern Recognition
Activity recognition via user-trace segmentation
ACM Transactions on Sensor Networks (TOSN)
Logical Hierarchical Hidden Markov Models for Modeling User Activities
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Efficient duration and hierarchical modeling for human activity recognition
Artificial Intelligence
Hierarchical multi-channel hidden semi Markov models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Three layered hidden Markov models for binary digital wireless channels
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Trajectory classification using switched dynamical hidden Markov models
IEEE Transactions on Image Processing
HMM based semi-supervised learning for activity recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
Discrete relative states to learn and recognize goals-based behaviors of groups
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Hierarchical multi-channel hidden semi Markov graphical models for activity recognition
Computer Vision and Image Understanding
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The hierarchical hidden Markov model (HHMM) is an extension of the hidden Markov model to include a hierarchy of the hidden states. This form of hierarchical modeling has been found useful in applications such as handwritten character recognition, behavior recognition, video indexing, and text retrieval. Nevertheless, the state hierarchy in the original HHMM is restricted to a tree structure. This prohibits two different states from having the same child, and thus does not allow for sharing of common substructures in the model. In this paper, we present a general HHMM in which the state hierarchy can be a lattice allowing arbitrary sharing of substructures. Furthermore, we provide a method for numerical scaling to avoid underflow, an important issue in dealing with long observation sequences. We demonstrate the working of our method in a simulated environment where a hierarchical behavioral model is automatically learned and later used for recognition.