  In this survey paper, we analyze image based graph neural networks and
propose a three-step classification approach. We first convert the image into
superpixels using the Quickshift algorithm so as to reduce 30% of the input
data. The superpixels are subsequently used to generate a region adjacency
graph. Finally, the graph is passed through a state-of-art graph convolutional
neural network to get classification scores. We also analyze the spatial and
spectral convolution filtering techniques in graph neural networks.
Spectral-based models perform better than spatial-based models and classical
CNN with lesser compute cost.
