  Due to a huge volume of information in many domains, the need for
classification methods is imperious. In spite of many advances, most of the
approaches require a large amount of labeled data, which is often not
available, due to costs and difficulties of manual labeling processes. In this
scenario, unsupervised and semi-supervised approaches have been gaining
increasing attention. The GCNs (Graph Convolutional Neural Networks) represent
a promising solution since they encode the neighborhood information and have
achieved state-of-the-art results on scenarios with limited labeled data.
However, since GCNs require graph-structured data, their use for
semi-supervised image classification is still scarce in the literature. In this
work, we propose a novel approach, the Manifold-GCN, based on GCNs for
semi-supervised image classification. The main hypothesis of this paper is that
the use of manifold learning to model the graph structure can further improve
the GCN classification. To the best of our knowledge, this is the first
framework that allows the combination of GCNs with different types of manifold
learning approaches for image classification. All manifold learning algorithms
employed are completely unsupervised, which is especially useful for scenarios
where the availability of labeled data is a concern. A broad experimental
evaluation was conducted considering 5 GCN models, 3 manifold learning
approaches, 3 image datasets, and 5 deep features. The results reveal that our
approach presents better accuracy than traditional and recent state-of-the-art
methods with very efficient run times for both training and testing.
