Publications – Visualization Research Group

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.

J. Schmidt, M. E. Gröller, and S. Bruckner, "VAICo: Visual Analysis for Image Comparison," IEEE Transactions on Visualization and Computer Graphics, vol. 19, iss. 12, p. 2090–2099, 2013. doi:10.1109/TVCG.2013.213
[BibTeX]

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.

@ARTICLE {Schmidt-2013-VVA,
author = "Johanna Schmidt and Meister 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",
number = "12",
pages = "2090--2099",
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.",
pdf = "pdfs/Schmidt-2013-VVA.pdf",
images = "images/Schmidt-2013-VVA.jpg",
thumbnails = "images/Schmidt-2013-VVA.png",
youtube = "https://www.youtube.com/watch?v=wfBqKZLVszk",
doi = "10.1109/TVCG.2013.213",
event = "IEEE VIS 2013",
keywords = "focus+context visualization, image set comparison, comparative visualization",
url = "//www.cg.tuwien.ac.at/research/publications/2013/schmidt-2013-vaico/"
}

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//www.cg.tuwien.ac.at/research/publications/2013/schmidt-2013-vaico/