Structure-aware Visualization Retrieval
This work addresses the need for improved visualization retrieval to benefit applications like recommendation, though it appears incremental by adding structural information to existing visual-based approaches.
The paper tackled the problem of retrieving perceptually similar visualizations from a large corpus by proposing a structure-aware method that incorporates both visual and structural information from SVG-based visualizations, resulting in demonstrated effectiveness and advantages over existing methods.
With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structure-aware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.