We propose a simple yet effective metric that measures structural similarity between visual instances of architectural floor plans, without the need for learning. Qualitatively, our experiments show that the retrieval results are similar to deeply learned methods. Effectively comparing instances of floor plan data is paramount to the success of machine understanding of floor plan data, including the assessment of floor plan generative models and floor plan recommendation systems. Comparing visual floor plan images goes beyond a sole pixel-wise visual examination and is crucially about similarities and differences in the shapes and relations between subdivisions that compose the layout. Currently, deep metric learning approaches are used to learn a pair-wise vector representation space that closely mimics the structural similarity, in which the models are trained on similarity labels that are obtained by Intersection-over-Union (IoU). To compensate for the lack of structural awareness in IoU, graph-based approaches such as Graph Matching Networks (GMNs) are used, which require pairwise inference for comparing data instances, making GMNs less practical for retrieval applications. In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed based on both image and graph distances. In addition, an efficient algorithm is developed that uses SSIG to rank a large-scale floor plan database. Code will be openly available.

翻译：我们提出了一种简单而有效的度量方法，可在无需学习的情况下衡量建筑平面图视觉实例之间的结构相似性。定性实验表明，检索结果与深度学习方法相当。有效比较平面图数据实例对于平面图数据的机器理解至关重要，包括评估平面图生成模型和平面图推荐系统。比较视觉平面图图像不仅涉及逐像素的视觉检查，更关键在于构成布局的各个区域之间形状与关系的相似性与差异性。目前，深度度量学习方法被用于学习一个紧密模拟结构相似性的成对向量表示空间，其中模型基于交并比（IoU）获得的相似性标签进行训练。为弥补IoU缺乏结构感知能力的不足，研究者采用图匹配网络（GMN）等图方法，但这类方法需对数据实例进行成对推理，导致GMN在检索应用中实用性较低。本文基于图像距离和图距离，提出一种用于评判平面图结构相似性的有效评估指标——SSIG（基于IoU和GED的结构相似性），并开发了一种利用SSIG对大规模平面图数据库进行排序的高效算法。代码将开源。