A trainable hierarchical hiddens Markov tree model for color image annotation

被引：0

作者：

Cheng, Li ^{[1
]}

Caelli, Terry ^{[1
]}

Ochoa, Victor ^{[1
]}

机构：

[1] Department of Computing Science, Research Institute for Multimedia Systems (RIMS), University of Alberta, Edmonton, Alta. T6G 2E9, Canada

来源：

Proceedings - International Conference on Pattern Recognition | 2002年 / 16卷 / 01期

关键词：

Algorithms - Color image processing - Markov processes - Mathematical models - Trees (mathematics);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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 Markavian models for image processing using multiscale representations. In this paper we explore a new bijective 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. © 2002 IEEE.

引用

页码：192 / 195

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