A Graph Convolutional Network for Visual Categorization

Abstract

The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors for image classification. Recently, Graph Convolutional Networks (GCNs) have also been witnessed in achieving improved performances for visual classification in various domains. A typical GCN is pertinent for propagating deep features using graph-based message passing methods. There are several domains such as the disease diagnosis of humans and plants where GCN could be explored for further performance enhancement. Thus, ample research attention is essential for solving different kinds of visual classification problems. In this direction, this work integrates the benefits of CNN and GCN for improving the feature representation by building a spatial relation using a GCN. In this work, a simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, a GCN is applied for enhancing feature representation capabilities further for image classification. The proposed method has achieved improved performances on seven benchmark public datasets representing dance postures, hand shapes, agriculture, medical imaging, and aerial scene classification. The proposed method is developed using four different CNN backbones. Particularly, the proposed method based on ResNet-50 backbone has attained 89.98% accuracy on Dance-12, 90.34% accuracy on REST hand shape, 94.06% accuracy on Kvasir, and 75.89% accuracy on ISIC skin cancer, 91.73% accuracy on AID aerial scene classification, and 95.24% accuracy on PlantPathology datasets.