Experiments in constructing belief networks for image classification systems
We present procedures and experimental results in constructing belief networks for image classification systems based on probabilistic reasoning. In particular, we compare the performance of systems based on manually constructed and automatically constructed belief networks. The systems exploit existing image descriptions and also exploit interactions between multiple classifiers to improve classification performance. Performance evaluation results for the consumer photography domain are presented.