Efficient Hidden Semi-Markov Model Inference for Structured Video Sequences

Authors:
D. Tweed;R. Fisher;J. Bins;T. List
Affiliations:
University of Edinburgh, Institute for Perception, Action & Behaviour, School of Informatics, James Clerk Maxwell Bldg, Kings Bldgs, Mayfield Road, Edinburgh EH9 3JZ. dtweed@inf.ed.ac.uk;Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA;Corp. Res. Adv. Eng. Multimedia, Robert Bosch GmbH, Stuttgart, Germany;Corp. Res. Adv. Eng. Multimedia, Robert Bosch GmbH, Stuttgart, Germany
Venue:
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Year:
2005

Citing 0
Cited 3

Structured Hidden Markov Model: A General Framework for Modeling Complex Sequences

AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Summarizing speech without text using hidden Markov models

NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Modeling Ant Activity by Means of Structured HMMs

ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The semantic interpretation of video sequences by computer is often formulated as probabilistically relating lower-level features to higher-level states, constrained by a transition graph. Using Hidden Markov Models inference is efficient but time-in-state data cannot be included, whereas using Hidden Semi-Markov Models we can model duration but have inefficient inference. We present a new efficient 0(T) algorithm for inference in certain HSMMs and show experimental results on video sequence interpretation in television footage to demonstrate that explicitly modelling time-in-state improves interpretation performance.