Neighbor Embedding Projection and Graph Convolutional Networks for Image Classification

Gustavo Rosseto Leticio, Vinicius Atsushi Sato Kawai, Lucas Valem, Daniel Pedronette

2025

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.

Paper Citation

in Harvard Style

Leticio G., Kawai V., Valem L. and Pedronette D. (2025). Neighbor Embedding Projection and Graph Convolutional Networks for Image Classification. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 511-518. DOI: 10.5220/0013260500003912

in Bibtex Style

@conference{visapp25,
author={Gustavo Leticio and Vinicius Kawai and Lucas Valem and Daniel Pedronette},
title={Neighbor Embedding Projection and Graph Convolutional Networks for Image Classification},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={511-518},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013260500003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Neighbor Embedding Projection and Graph Convolutional Networks for Image Classification
SN - 978-989-758-728-3
AU - Leticio G.
AU - Kawai V.
AU - Valem L.
AU - Pedronette D.
PY - 2025
SP - 511
EP - 518
DO - 10.5220/0013260500003912
PB - SciTePress