Visual abstraction and exploration of multi-class scatterplots

Haidong Chen, Wei Chen, Honghui Mei, Zhiqi Liu, Kun Zhou, Weifeng Chen, Wentao Gu, Kwan-Liu Ma

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.

Original languageEnglish (US)
Article number6875982
Pages (from-to)1683-1692
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume20
Issue number12
DOIs
StatePublished - Dec 31 2014

Keywords

  • overdraw reduction
  • sampling
  • Scatterplot
  • visual abstraction

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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