VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL

Joakim Jitén, Bernard Merialdo

Abstract

Statistical modeling methods have become critical for many image processing problems, such as segmentation, compression and classification. In this paper we are proposing and experimenting a computationally efficient simplification of 3-Dimensional Hidden Markov Models. Our proposed model relaxes the dependencies between neighboring state nodes to a random uni-directional dependency by introducing a three dimensional dependency tree (3D-DT HMM). To demonstrate the potential of the model we apply it to the problem of tracking objects in a video sequence. We explore various issues about the effect of the random tree and smoothing techniques. Experiments demonstrate the potential of the model as a tool for tracking video objects with an efficient computational cost.

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Paper Citation


in Harvard Style

Jitén J. and Merialdo B. (2007). VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 191-198. DOI: 10.5220/0002044701910198


in Bibtex Style

@conference{visapp07,
author={Joakim Jitén and Bernard Merialdo},
title={VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={191-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002044701910198},
isbn={978-972-8865-73-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL
SN - 978-972-8865-73-3
AU - Jitén J.
AU - Merialdo B.
PY - 2007
SP - 191
EP - 198
DO - 10.5220/0002044701910198