  We present an application of the Layer-wise Relevance Propagation (LRP)
algorithm to state of the art deep convolutional neural networks and Fisher
Vector classifiers to compare the image perception and prediction strategies of
both classifiers with the use of visualized heatmaps. Layer-wise Relevance
Propagation (LRP) is a method to compute scores for individual components of an
input image, denoting their contribution to the prediction of the classifier
for one particular test point. We demonstrate the impact of different choices
of decomposition cut-off points during the LRP-process, controlling the
resolution and semantics of the heatmap on test images from the PASCAL VOC 2007
test data set.
