A trainable hierarchical hidden Markov tree model for color image annotation
Li Cheng
Caelli, T.
Ochoa, V.
Dept. of Comput. Sci., Alberta Univ., Edmonton, Alta., Canada;
This paper appears in: Pattern Recognition, 2002. Proceedings. 16th International Conference on
Publication Date: 2002
Volume: 1,
On page(s): 192- 195 vol.1
ISSN: 1051-4651
ISBN: 0-7695-1695-X
INSPEC Accession Number: 7461370
Digital Object Identifier: 10.1109/ICPR.2002.1044648
Current Version Published: 2002-12-10
Abstract
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 bijective tip-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.
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