VAICo: Visual Analysis for Image Comparison

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • ViMaL: A Visualization Mapping Language
• Date: December 2013
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 19
• Number: 12
• Note: Demo: http://www.cg.tuwien.ac.at/~jschmidt/vaico/
• Lecturer: Johanna Schmidt
• Conference date: 13. October 2013 – 18. October 2013
• Pages: 2090 – 2099
• Keywords: focus+context, image-set comparison, Comparative visualization

Abstract

Scientists, engineers, and analysts are confronted with ever larger and more complex sets of data, whose analysis poses special challenges. In many situations it is necessary to compare two or more datasets. Hence there is a need for comparative visualization tools to help analyze differences or similarities among datasets. In this paper an approach for comparative visualization for sets of images is presented. Well-established techniques for comparing images frequently place them side-by-side. A major drawback of such approaches is that they do not scale well. Other image comparison methods encode differences in images by abstract parameters like color. In this case information about the underlying image data gets lost. This paper introduces a new method for visualizing differences and similarities in large sets of images which preserves contextual information, but also allows the detailed analysis of subtle variations. Our approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this process are then presented in an interactive web application which allows users to rapidly explore the space of differences and drill-down on particular features. We demonstrate the flexibility of our approach by applying it to multiple distinct domains.

Additional Files and Images

Weblinks

• http://dx.doi.org/10.1109/TVCG.2013.213

BibTeX

@article{vaico,
  title =      "VAICo: Visual Analysis for Image Comparison",
  author =     "Johanna Schmidt and Eduard Gr\"{o}ller and Stefan Bruckner",
  year =       "2013",
  abstract =   "Scientists, engineers, and analysts are confronted with ever
               larger and more complex sets of data, whose analysis poses
               special challenges. In many situations it is necessary to
               compare two or more datasets. Hence there is a need for
               comparative visualization tools to help analyze differences
               or similarities among datasets. In this paper an approach
               for comparative visualization for sets of images is
               presented. Well-established techniques for comparing images
               frequently place them side-by-side. A major drawback of such
               approaches is that they do not scale well. Other image
               comparison methods encode differences in images by abstract
               parameters like color. In this case information about the
               underlying image data gets lost. This paper introduces a new
               method for visualizing differences and similarities in large
               sets of images which preserves contextual information, but
               also allows the detailed analysis of subtle variations. Our
               approach identifies local changes and applies cluster
               analysis techniques to embed them in a hierarchy. The
               results of this process are then presented in an interactive
               web application which allows users to rapidly explore the
               space of differences and drill-down on particular features.
               We demonstrate the flexibility of our approach by applying
               it to multiple distinct domains.",
  month =      dec,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "19",
  number =     "12",
  note =       "Demo: http://www.cg.tuwien.ac.at/~jschmidt/vaico/",
  pages =      "2090--2099",
  keywords =   "focus+context, image-set comparison, Comparative
               visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2013/vaico/",
}