  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.
