University of Bergen | Faculty of Mathematics and Natural Sciences | Department of Informatics | Visualization Group
Visualization
You are here: Department of Informatics > Visualization Group > Publications > Schmidt-2013-VVA
 Visualization
 > about
 > team & contact info
 > research
 > publications
 > projects
 > teaching
 > seminars
 > resources
 > network
 > events
 > links

VAICo: Visual Analysis for Image Comparison

Johanna Schmidt, M. Eduard Gröller, Stefan Bruckner

ARTICLE, IEEE Transactions on Visualization and Computer Graphics, dec, 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.

Published

IEEE Transactions on Visualization and Computer Graphics

Media

  • paper
  • www
  • Click to view

BibTeX

@ARTICLE{Schmidt-2013-VVA,
  author = {Johanna Schmidt and M. Eduard Gr{\"o}ller and Stefan Bruckner},
  title = {VAICo: Visual Analysis for Image Comparison},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2013},
  volume = {19},
  pages = {2090--2099},
  number = {12},
  month = dec,
  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.},
  event = {IEEE VIS 2013},

  keywords = {focus+context visualization, image set comparison, comparative visualization},

  url = {http://www.cg.tuwien.ac.at/research/publications/2013/schmidt-2013-vaico/}
}






 Last Modified: Jean-Paul Balabanian, 2014-09-11