IEEE Transactions on Visualization and Computer Graphics (TVCG - InfoVis), 2009

Document Cards: A Top Trumps Visualization for Documents

Hendrik Strobelt    Daniela Oelke    Christian Rohrdantz    Andreas Stoffel    Oliver Deussen    Daniel Keim
University of Konstanz, Germany

Abstract

Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images.

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