Feature Relevance in Bayesian Network Classifiers and Application to Image Event Recognition

An important problem in Bayesian networks classifiers (BNC) is to discover relevant variables that can achieve optimal classification performance. We propose a method based on Bayesian inference for estimating and incorporating feature relevance in classification using BNCs. We empirically validate our method on an application to event recognition in natural images using object and scene information.