发表机构
Indian Institute of Science Education and Research, Thiruvanantapuram; Indian Institute of Science Education and Research, Kolkata（印度科学教育与研究学院蒂鲁文南特普拉姆校区; 印度科学教育与研究学院加尔各答校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了包含超过11,000张标注图像的新数据集，对比经典机器学习与深度学习模型，发现VGG16在拓扑关系识别中验证准确率达89.55%，验证了迁移学习的有效性，为空间推理研究提供了新基准。
AI 中文摘要
弄清楚物体之间如何相互关联，例如它们是接触、重叠、完全分离还是其中一个位于另一个内部，在地理信息系统、生物医学成像和机器人技术等领域至关重要。尽管机器学习已经取得了长足进步，但人们并未真正关注图像中这些拓扑关系的识别。主要障碍是什么？缺乏足够好的数据集，且没有明确的衡量结果的方法。因此，我们撸起袖子构建了一个新数据集。该数据集相当可观：包含超过11,000张标注图像，展示了所有那些基本关系。我们使用一些经典机器学习模型（朴素贝叶斯、KNN、随机森林、SVM和人工神经网络）进行了测试，并引入了像VGG16和InceptionResNetV2这样的深度学习明星模型。对于数据集本身，我们使用分割、轮廓检测和灰度归一化来提取稳健的特征向量。结果如何？深度学习方法，尤其是VGG16，表现突出，验证准确率达到89.55%。与传统模型相比，这是一个巨大的飞跃。这表明迁移学习在分析图像中的拓扑关系方面具有强大能力，并为研究人员在未来空间推理和拓扑分类的工作中提供了一个新的基准。
英文摘要
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.
