Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation

2026-06-15

AI Summary

This work addresses the challenge of semi-supervised image classification under limited labeled data by proposing a novel approach that integrates multi-source features with graph-structured representations. The method constructs a multi-graph representation by fusing complementary features extracted from convolutional neural networks (CNNs), Vision Transformers (ViTs), and graph convolutional networks (GCNs), and refines the graph structure through manifold learning. A key innovation is the introduction of a ranking-based aggregation mechanism to effectively integrate heterogeneous feature sources. Extensive experiments demonstrate that the proposed framework consistently achieves significant improvements in classification accuracy across various settings, thereby validating the efficacy of the multi-feature fusion strategy and the graph optimization scheme.

Abstract

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.

Problem

• semi-supervised image classification
• feature extraction
• graph neural networks
• multi-feature aggregation
• limited labeled data

Innovation

• Graph Neural Networks
• Multi-Feature Aggregation
• Semi-Supervised Learning
• Rank Aggregation
• Manifold Learning