Classifier training based on synthetically generated samples

Abstract

In most image classification systems, the amount and quality of the training samples used to represent the different pattern classes are important factors governing the recognition performance. Hence, it is usually necessary to acquire a representative set of training samples by acquisition of data in real-world environments. Such procedures may require considerable efforts and furthermore often generate a training set which is unbalanced with respect to the number of available samples per class. In this contribution ..