Structure-aware Visualization Retrieval

TL;DR

This paper introduces a structure-aware visualization retrieval method that leverages both visual appearance and inherent structural information of SVG-based visualizations to improve retrieval accuracy.

Contribution

It presents a novel approach that incorporates structural information in SVG visualizations, enhancing retrieval performance over existing appearance-only methods.

Findings

Outperforms existing methods in retrieval accuracy

Validated through quantitative comparisons and user studies

Effective in capturing both visual and structural features

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 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.