Neighbor Embedding Projection and Graph Convolutional Networks for Image Classification

Abstract

The exponential increase in image data has heightened the need for machine learning applications, particularly in image classification across various fields. However, while data volume has surged, the availability of labeled data remains limited due to the costly and time-intensive nature of labeling. Semi-supervised learning offers a promising solution by utilizing both labeled and unlabeled data; it employs a small amount of labeled data to guide learning on a larger unlabeled set, thus reducing the dependency on extensive labeling efforts. Graph Convolutional Networks (GCNs) introduce an effective method by applying convolutions in graph space, allowing information propagation across connected nodes. This technique captures individual node features and inter-node relationships, facilitating the discovery of intricate patterns in graph-structured data. Despite their potential, GCNs remain underutilized in image data scenarios, where input graphs are often computed using features extracted from pre-trained models without further enhancement. This work proposes a novel GCN-based approach for image classification, incorporating neighbor embedding projection techniques to refine the similarity graph and improve the latent feature space. Similarity learning approaches, commonly employed in image retrieval, are also integrated into our workflow. Experimental evaluations across three datasets, four feature extractors, and three GCN models revealed superior results in most scenarios.