Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification

Abstract

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.

References in corpus (11)

• Scikit-learn: Machine Learning in Python
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Comprehensive Survey on Graph Neural Networks
• Semi-Supervised Classification with Graph Convolutional Networks
• Active Learning for Convolutional Neural Networks: A Core-Set Approach
• Graph Neural Networks with convolutional ARMA filters
• Scaling Graph Neural Networks with Approximate PageRank
• Learning Discrete Structures for Graph Neural Networks
• Semi-Supervised Cross-Modal Retrieval with Label Prediction
• Active Learning: Problem Settings and Recent Developments
• SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning

Cited by in corpus (2)

• Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data
• Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification