Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy.

翻译：特征提取涉及识别并提取显著特征或模式，包括边缘、纹理、形状和颜色属性。当代特征提取器主要依赖深度学习架构，如卷积神经网络（CNN）和视觉变换器（ViT）。文献中多样化的特征提取器提供了丰富的特征表示。从图像中提取的特征取决于具体应用、所选提取器及其配置。因此，通过组合不同提取器来整合互补信息，为提升性能提供了可行途径。图神经网络（GNN），尤其是图卷积网络（GCN），已成为半监督图像分类中强大且广泛采用的方法——它们能有效利用标记与未标记数据，并借助捕获样本间关系的底层图结构。本研究针对标记数据稀缺的场景，提出一种新颖的GNN方法，通过整合来自多种提取器的多样化特征和图表示进行分类。实验研究涵盖不同特征与图提取器的组合，以及排序聚合策略。实验结果凸显了本研究的主要贡献：策略性地结合特征与图表示，并应用流形学习进行图处理，能在大多数实验条件下显著提升分类准确率。此外，采用排序聚合技术整合不同提取器的特征，也被证明能提高分类准确率。