  Image fusion methods and metrics for their evaluation have conventionally
used pixel-based or low-level features. However, for many applications, the aim
of image fusion is to effectively combine the semantic content of the input
images. This paper proposes a novel system for the semantic combination of
visual content using pre-trained CNN network architectures. Our proposed
semantic fusion is initiated through the fusion of the top layer feature map
outputs (for each input image)through gradient updating of the fused image
input (so-called image optimisation). Simple "choose maximum" and "local
majority" filter based fusion rules are utilised for feature map fusion. This
provides a simple method to combine layer outputs and thus a unique framework
to fuse single-channel and colour images within a decomposition pre-trained for
classification and therefore aligned with semantic fusion. Furthermore, class
activation mappings of each input image are used to combine semantic
information at a higher level. The developed methods are able to give
equivalent low-level fusion performance to state of the art methods while
providing a unique architecture to combine semantic information from multiple
images.
