Structure-aware Visualization Retrieval

Abstract

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 structureaware 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.

ComputableViz: Mathematical Operators as a Formalism for Visualization Processing and Analysis

“…In surveying relevant literature, we observe that a large body of research has moved from operating on a single visualization to multiple visualizations. For example, style transfer involves "merging" a content visualization with a style visualization [23], and example-based retrieval concerns measuring the difference or similarity between visualizations [29,45].…”