Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

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 beneft 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 efectiveness of our approach and its advantages over existing methods.

Keywords

Data visualization, Visualization retrieval, Visualization similarity, Representation learning, Visualization embedding

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management

Publication

Conference Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Los Angeles, USA, April 30 - May 5

First Page

1

Last Page

14

ISBN

9781450391573

Identifier

10.1145/3491102.3502048

Publisher

Association for Computing Machinery

City or Country

New York

Additional URL

https://doi.org/10.1145/3491102.3502048

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