Exploring Learning Models for Topological Relationship Recognition from Image Data

2026-09-28

AI Summary

This study addresses the lack of datasets and evaluation standards for recognizing topological relationships between objects in images by constructing the first large-scale topological relationship dataset comprising 11,000 annotated images. Methodologically, feature extraction techniques such as image segmentation and contour detection are integrated to systematically evaluate both traditional machine learning algorithms and deep transfer learning models, including VGG16 and InceptionResNetV2. The research establishes a new evaluation benchmark for this task and validates the effectiveness of transfer learning in spatial reasoning. Notably, the VGG16 model achieves an accuracy of 89.55%, significantly outperforming conventional methods. These contributions provide a foundational dataset and a performance benchmark for future research in visual topological reasoning.

Abstract

Figuring out how objects relate to each other, like whether they touch, overlap, stay completely separate or one sits inside another, matters a lot in fields like GIS, biomedical imaging, and robotics. Even though machine learning has come a long way, people haven't really focused on spotting these topological relationships in images. The main roadblocks? Not enough good datasets and no clear way to measure results. So, we rolled up our sleeves and built a new dataset. It's pretty sizable: over 11,000 labelled images showing all those essential relationships. We ran tests with some classic machine learning models, Naive Bayes, KNN, Random Forest, SVM, and Artificial Neural Networks, and threw in some deep learning stars like VGG16 and InceptionResNetV2. For the dataset itself, we used segmentation, contour detection, and grayscale normalization to tease out solid feature vectors. The results? Deep learning methods, especially VGG16, pulled ahead, with validation accuracy hitting 89.55%. That's a big jump compared to the traditional models. This shows how powerful transfer learning is for analyzing topological relationships in images, and it gives researchers a new standard to aim for in future work on spatial reasoning and topological classification.

Problem

• Topological Relationship Recognition
• Image Data
• Dataset Deficiency
• Spatial Reasoning
• Evaluation Metrics

Innovation

• Topological Relationship Recognition
• Transfer Learning
• Image Segmentation
• Spatial Reasoning
• Dataset Construction