Fragmented Image Classification Using Local and Global Neural Networks: Investigating the Impact of the Quantity of Artificial Objects on Model Performance

This paper addresses the challenge of classifying objects based on fragmented data, particularly when dealing with characteristics extracted from images captured from various angles. The complexity increases when dealing with fragmented images that may partially overlap. The paper introduces a classification model utilizing neural networks, specifically multilayer perceptron (MLP) networks. The key concept involves generating local models based on local tables comprising characteristics extracted from fragmented images. Since the local tables may have different sets of attributes due to varying perspectives, missing attributes in the tables are imputed by introducing artificial objects. The local models, now with identical structures are created and the aggregation of these models into a global model is carried out using weighted averages. The model’s efficacy is evaluated against existing literature methods using various metrics, demonstrating superior performance in terms of F-measure and balanced accuracy. Notably, the paper investigates the impact of the number of generated artificial objects on classification quality, revealing that a higher number generally improves results.