Bayesian network structure learning and inference in indoor vs. outdoor image classification

Abstract

Bayesian network model selection techniques may be used to learn and elucidate conditional relationships between features in pattern recognition tasks. The learned Bayesian network may then be used to infer unknown node-states, which may correspond to semantic tasks. One such application of this framework is scene categorization. In this paper, we employ low-level classification based on color and texture, semantic features, such as sky and grass detection, along with indoor vs. outdoor ground truth information, to create a feature set for Bayesian network structure learning. Indoor vs. outdoor inference may then be performed on a set of features derived from a testing set where node states are unknown. Experimental results show that this technique provides classification rates of 97% correct, which is a significant improvement over previous work, where a Bayesian network was constructed based on expert opinion.