Ye Yilin, Huang Rong, Zeng Wei
IEEE Trans Vis Comput Graph. 2024 Jul;30(7):3224-3240. doi: 10.1109/TVCG.2022.3229023. Epub 2024 Jun 27.
High-quality visualization collections are beneficial for a variety of applications including visualization reference and data-driven visualization design. The visualization community has created many visualization collections, and developed interactive exploration systems for the collections. However, the systems are mainly based on extrinsic attributes like authors and publication years, whilst neglect intrinsic property (i.e., visual appearance) of visualizations, hindering visual comparison and query of visualization designs. This paper presents VISAtlas, an image-based approach empowered by neural image embedding, to facilitate exploration and query for visualization collections. To improve embedding accuracy, we create a comprehensive collection of synthetic and real-world visualizations, and use it to train a convolutional neural network (CNN) model with a triplet loss for taxonomical classification of visualizations. Next, we design a coordinated multiple view (CMV) system that enables multi-perspective exploration and design retrieval based on visualization embeddings. Specifically, we design a novel embedding overview that leverages contextual layout framework to preserve the context of the embedding vectors with the associated visualization taxonomies, and density plot and sampling techniques to address the overdrawing problem. We demonstrate in three case studies and one user study the effectiveness of VISAtlas in supporting comparative analysis of visualization collections, exploration of composite visualizations, and image-based retrieval of visualization designs. The studies reveal that real-world visualization collections (e.g., Beagle and VIS30K) better accord with the richness and diversity of visualization designs than synthetic collections (e.g., Data2Vis), inspiring composite visualizations are identified in real-world collections, and distinct design patterns exist in visualizations from different sources.
高质量的可视化集合对包括可视化参考和数据驱动的可视化设计在内的各种应用都有益处。可视化社区已经创建了许多可视化集合，并为这些集合开发了交互式探索系统。然而，这些系统主要基于作者和出版年份等外在属性，而忽略了可视化的内在属性（即视觉外观），这阻碍了可视化设计的视觉比较和查询。本文提出了VISAtlas，一种基于图像的方法，由神经图像嵌入技术赋能，以促进对可视化集合的探索和查询。为了提高嵌入精度，我们创建了一个包含合成和真实世界可视化的综合集合，并用它来训练一个带有三元组损失的卷积神经网络（CNN）模型，用于可视化的分类学分类。接下来，我们设计了一个协调多视图（CMV）系统，该系统能够基于可视化嵌入进行多视角探索和设计检索。具体来说，我们设计了一种新颖的嵌入概述，它利用上下文布局框架来保留嵌入向量与相关可视化分类法的上下文，并使用密度图和采样技术来解决重叠问题。我们在三个案例研究和一个用户研究中展示了VISAtlas在支持可视化集合的比较分析、复合可视化的探索以及基于图像的可视化设计检索方面的有效性。研究表明，与合成集合（如Data2Vis）相比，真实世界的可视化集合（如Beagle和VIS30K）更符合可视化设计的丰富性和多样性，在真实世界集合中识别出了启发性的复合可视化，并且来自不同来源的可视化中存在不同的设计模式。
