  The intermediate map responses of a Convolutional Neural Network (CNN)
contain information about an image that can be used to extract contextual
knowledge about it. In this paper, we present a core sampling framework that is
able to use these activation maps from several layers as features to another
neural network using transfer learning to provide an understanding of an input
image. Our framework creates a representation that combines features from the
test data and the contextual knowledge gained from the responses of a
pretrained network, processes it and feeds it to a separate Deep Belief
Network. We use this representation to extract more information from an image
at the pixel level, hence gaining understanding of the whole image. We
experimentally demonstrate the usefulness of our framework using a pretrained
VGG-16 model to perform segmentation on the BAERI dataset of Synthetic Aperture
Radar(SAR) imagery and the CAMVID dataset.
