Component Optimization for Image Understanding: A Bayesian Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning for adaptive image interpretation
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
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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.