Graph Neural Networks Based Approach for Interpersonal Relationship Classification in Images, Imgelerde Kişiler Arasi Ilişki Siniflandirmasi için Grafik Sinir Aǧlari Tabanli Yaklaşim

Abstract

Interpersonal relationships, which are an integral part of our social life, demonstrate how people connect and interact within society. Describing the relationship between two individuals in images depends on many different factors and attributes. In this study, a Graph Neural Network (GNN) based approach is proposed that focuses on the important attributes for describing the relationship between two individuals in images. In the proposed method, each attribute that will be used to describe the relationship is defined as a GNN node. Then, the meaningless connections between nodes are pruned with a pruning operation to obtain the ideal GNN model. In this study, a more robust GNN for classifying interpersonal relationships is created by using rich attributes, unlike the literature, and obtaining significant connections between nodes through pruning. The experiments conducted in this study showed that both using a wider GNN and pruning operation improve the classification performance. © 2023 Elsevier B.V., All rights reserved.