  Recent enhancements of deep convolutional neural networks (ConvNets)
empowered by enormous amounts of labeled data have closed the gap with human
performance for many object recognition tasks. These impressive results have
generated interest in understanding and visualization of ConvNets. In this
work, we study the effect of background in the task of image classification.
Our results show that changing the backgrounds of the training datasets can
have drastic effects on testing accuracies. Furthermore, we enhance existing
augmentation techniques with the foreground segmented objects. The findings of
this work are important in increasing the accuracies when only a small dataset
is available, in creating datasets, and creating synthetic images.
