Dependency Tree Hidden Markov Models
Joakim Jiten, Bernard Merialdo
2003-2008
Project description
We are studying a new type of multi-dimensional Hidden Markov Model.
Multidimensional HMMs have an exponential complexity which makes them unusable in practise.
The main idea of DT-HMM is to replace the multiple dependency of the neighbours (2 neighbours in the case of 2 dimensions) with a single dependency with a randomly selected neighbour.
Multiple dependencies can be weakly taken into account by the use of several dependency trees.
We have developped a library to manipulate DT-HMMs and applied them to a number of different tasks, such as:
- image segmentation,
- semantic classification,
- semantic region annotation,
- video object tracking.
Publications
- Jiten, Joakim; Merialdo, Bernard
Video modeling using 3-D Hidden Markov model
VISAPP 2007, 2nd International Conference on Computer Vision Theory and Applications,
8 - 11 March, 2007 Barcelona, Spain
- Jiten, Joakim; Merialdo, Bernard
Semantic image segmentation with a multi-dimensional Hidden Markov Model
MMM’07, International MultiMedia Modeling Conference,
January 9-12 2007, Singapore - Also published as LNCS Volume 4351
- Merialdo, Bernard; Jiten, Joakim; Galmar, Eric; Huet, Benoit
A new approach to probabilistic image modeling with multidimensional hidden Markov models
AMR 2006, 4th International Workshop on Adaptive Multimedia Retrieval
27-28 July 2006, Geneva, Switzerland |Also published as LNCS Volume 4398
- Jiten, Joakim; Merialdo, Bernard; Huet, Benoit
Multi-dimensional dependency-tree hidden Markov models
ICASSP 2006, 31st IEEE International Conference on Acoustics, Speech, and Signal Processing,
May 14-19, 2006, Toulouse, France
- Jiten, Joakim; Merialdo, Bernard
Probabilistic image modeling with dependency-tree hidden Markov models
WIAMIS 2006, 7th International Workshop on Image Analysis for Multimedia Interactive Services,
April 19-21, 2006, Incheon, Korea
- Jiten, Joakim; Huet, Benoit; Merialdo, Bernard
Semantic feature extraction with multidimensional hidden Markov model
SPIE Conference on Multimedia Content Analysis, Management and Retrieval 2006, January 17-19, 2006 - San Jose, USA - SPIE proceedings Volume 6073 Volume 6073 , pp 211-221
