An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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This paper introduces a classification method based on feature space segmentation. Since the classification task is equivalent to a probability distribution estimation, a Bayesian network is used as an inference mechanism for dealing with the underling probability distribution function that, presumably, is complex and factored. The article presents a method for splitting the feature space into regions that are associated to local classifiers. After that, a Bayesian network is used for combining their outputs. Experimental results reveal that this is a suitable approach for speeding up the training phase for large databases as well as to ensure good recognition rates.