Simultaneous learning of decision rules and important attributes for classification problems in image analysis

摘要

A new method for automatic machine learning of decision rules for classification problems in image analysis is presented. The method facilitates simultaneous decision rule inference and selection of discriminative features which characterize the image entities to be classified. The basis of the method is to approximate class conditional densities by a mixture of parameterized densities. Its performance is tested on a classification problem involving real image data.