Semantic-preservingword clouds by seam carving

Yingcai Wu, Thomas Provan, Furu Wei, Shixia Liu, Kwan-Liu Ma

Research output: Contribution to journalArticle

61 Scopus citations

Abstract

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.

Original languageEnglish (US)
Pages (from-to)741-750
Number of pages10
JournalComputer Graphics Forum
Volume30
Issue number3
DOIs
StatePublished - Jan 1 2011

Keywords

  • Categories and Subject Descriptors (according to ACM CCS)
  • I.3.6 [Computer Graphics]
  • Methodology and Techniques-Interaction Techniques

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design

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