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.

翻译：由于诸多领域信息量巨大，分类方法的需求日益迫切。尽管取得了诸多进展，大多数方法仍需要大量标注数据，而由于人工标注过程的成本和难度，这些数据往往难以获取。在此背景下，无监督和半监督方法正受到越来越多的关注。图卷积神经网络（GCNs）作为一种有前景的解决方案，能够编码邻域信息，并在标注数据有限的情况下取得了最先进的结果。然而，由于GCNs需要图结构数据，其在半监督图像分类中的应用在文献中仍然较少。本文提出了一种基于GCNs的新方法——Manifold-GCN，用于半监督图像分类。本文的主要假设是，利用流形学习来建模图结构可以进一步提升GCN的分类性能。据我们所知，这是首个将GCNs与不同类型的流形学习方法相结合用于图像分类的框架。所采用的所有流形学习算法完全无监督，这在标注数据可用性受限的场景中尤为有用。我们进行了广泛的实验评估，考虑了5种GCN模型、3种流形学习方法、3个图像数据集和5种深度特征。结果表明，我们的方法在训练和测试阶段均能达到比传统和最新最先进方法更高的准确率，且运行效率极高。