Inferring a Bayesian Network for Content-Based Image Classification

Bayesian networks are popular in the classification literature. The simplest kind of Bayesian network, i.e. naive Bayesian network, has gained the interest of many researchers because of quick learning and inferring. However, when there are lots of classes to be inferred from a similar set of evidences, one may prefer to have a united network. In this paper we present a new method for merging naive networks in order achieve a complete network and study the effect of this merging. The proposed method reduces the burden of learning a complete network. A simple measure is also introduced to assess the stability of the results after the combination of classifiers. The merging method is applied to the image classification problem. The results indicate that in addition to the reduced computation burden for learning a complete network, the total precision is increased and the precision alteration for each individual class is estimable using the measure.