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16th International Conference on Pattern Recognition (ICPR'02) - Volume 1
A Trainable Hierarchical Hidden Markov Tree Model for Color Image Annotation
Quebec City, QC, Canada
August 11-August 15
ISBN: 0-7695-1695-X
Li Cheng, University of Alberta
Terry Caelli, University of Alberta
Victor Ochoa, University of Alberta
In this paper we consider how to annotate or label regions of grey-level or multispectral images based upon known labels and a set of interacting hierarchical doubly stochastic processes. The proposed model extends current work on the use of hierarchical Markovian models for image processing using multiscale representations. In this paper we explore a new objective up-down algorithm whereby the spatio-spectral context of specific image region signatures are encoded via different types of trainable support kernels for the upward and downward Operations.
Index Terms:
Hierarchical Markovian models, Image Annotation
Citation:
Li Cheng, Terry Caelli, Victor Ochoa, "A Trainable Hierarchical Hidden Markov Tree Model for Color Image Annotation," icpr, vol. 1, pp.10192, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 1, 2002
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