Ensemble Learning

Abstract

The subject of this chapter is image fusion using the methods of ensemble learning. Ensemble learning is a method for constructing accurate predictors or classifiers from an ensemble of weak predictors or classifiers. In the context of image fusion, we use the term ensemble learning to denote the fusion of K input images I _k ,k ∈ {1,2, . . .,K}, where the I _k are all derived from the same base image I*. The I _k themselves highlight different features in I*. The theory of ensemble learning suggests that by fusing together the I _k we may obtain a fused image with a substantially improved quality. In the first part of the chapter we consider methods for constructing I _k . In the second part we consider methods for fusing the I _k .