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arXiv · 2106.06307

Survey of Image Based Graph Neural Networks

Usman Nazir·He Wang·Murtaza Taj
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Abstract

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.

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Usman Nazir, He Wang, Murtaza Taj. 2021-06-11. Survey of Image Based Graph Neural Networks. https://arxiv.org/abs/2106.06307

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